Dialogue State Tracking

A component of conversational systems that maintains a structured representation of the user’s goals and the context of a conversation across turns. It updates the dialogue state as new utterances are processed.

Semantic Classification

Content

Compositional Relationships (Components)

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Dependency Relationships

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Capability Relationships

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Implementation Relationships

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Reduction Relationships

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About

Dialogue State Tracking has been the locus of the most precise, benchmark-driven progress in the broader field of Dialogue Systems over the past decade. The fundamental problem — determining what the user wants at any moment during a conversation, in a form the rest of the dialogue pipeline can act on — is deceptively simple to state but remarkably hard to solve robustly. A belief state for a multi-domain system covering hotels, restaurants, and train bookings might require tracking 35 or more independent (domain, slot) pairs simultaneously, any subset of which may be active, modified, or implicitly constrained at any given turn. The complexity multiplies when the user makes implicit references (“somewhere near there”, “same price range as before”), when ASR errors corrupt key values, or when users shift domains mid-conversation without explicit signposting.

The intellectual lineage of DST begins with the Partially Observable Markov Decision Process (POMDP) formulation introduced by Steve Young and the Cambridge Dialogue Systems Group, which recast dialogue state as a probability distribution over a hidden state space to be inferred via Bayesian filtering. The Bayesian Update of Dialogue State (BUDS) framework (Young et al., 2010) and subsequent HIS (Hidden Information State) model provided the first mathematically coherent treatment of uncertainty in spoken dialogue — acknowledging that ASR outputs are noisy and that the system’s belief about what the user said should be maintained as a distribution rather than a point estimate. These probabilistic frameworks were computationally tractable only through aggressive state space factorisation assumptions, but they established the conceptual vocabulary — belief state, marginal belief, slot, domain — that persists in contemporary DST research.

The neural turn in DST began with the Neural Belief Tracker (NBT; Mrkšić et al., 2017), which replaced hand-crafted feature engineering with semantic similarity scoring between user utterances and candidate slot-value pairs using pre-trained word vectors. NBT made DST models domain-portable for the first time. The publication of the MultiWOZ dataset (Budzianowski et al., 2018) — containing over 10,000 goal-oriented dialogues across seven domains — catalysed a decade of benchmark-driven DST research, with models progressing through increasingly sophisticated transformer-based architectures: TRADE (Transferable Dialogue State Generator, Wu et al., 2019) introduced a copy mechanism enabling open-vocabulary slot value generation; TripPy (Heck et al., 2020) employed a triple copy strategy that separately handled value prediction, span extraction, and carry-over from prior turns; and SOM-DST (Kim et al., 2019) modelled state as a selectively overwritable memory. These architectural innovations, combined with BERT-based pre-training, drove JGA scores from roughly 25% (2018) to over 55% (2022-2023) on MultiWOZ 2.1.

The arrival of instruction-tuned Large Language Models opened a qualitatively new DST paradigm. Hu et al. (2022) demonstrated IC-DST, enabling few-shot dialogue state tracking through in-context examples. Feng et al. (2024) showed that LLMs can function as zero-shot DST modules via structured function calling — representing dialogue domains as typed function schemas embedded in the system prompt — achieving competitive performance with fine-tuned specialist models without any dialogue-specific training data. Interpretable DST via natural language summarisation (2025) has further shown that LLMs can generate human-readable dialogue summaries as intermediate representations, improving both explainability and tracking accuracy on complex multi-domain conversations. As of 2025, hybrid approaches combining lightweight specialist encoders (for speed and determinism) with LLM-based verification passes (for robustness to unusual phrasings) represent the emerging production architecture.

Components and Architecture

The internal architecture of a DST module varies substantially between the ontology-constrained classifier era (2013-2017), the neural encoder era (2017-2021), and the LLM era (2021-present), but the logical components remain consistent across all generations.

• Dialogue History Encoder: Transforms the full sequence of system and user utterances preceding the current turn into a dense contextual representation. BERT-based encoders (Devlin et al., 2019) treat the concatenated dialogue history as a long pseudo-document, with special separator tokens marking turn boundaries; this enables the model to use bidirectional attention across the entire conversation history, capturing long-range coreference and slot carry-over. Models like BERT-DST, SUMBT, and Dialogue-BERT fine-tuned on this concatenated representation established state-of-the-art on MultiWOZ 2.1 in 2019-2020. Recent LLM-based approaches directly condition on the full context window — potentially spanning 30-60 turns with system prompts, database query results, and schema descriptions — treating the entire conversation as a long-document question answering problem without explicit history encoding.

• Domain Classifier: Determines which dialogue domains (hotel, restaurant, taxi, train, attraction, hospital, police in MultiWOZ) are currently active at a given turn. Implemented as multi-label classification over a predefined domain ontology. Domain activation can be explicit (the user says “I need a hotel”) or implicit (a constraint on check-out date implies an active hotel booking sub-task). Multi-domain conversations require the classifier to detect domain switches and simultaneous multi-domain activity — for example, a user booking a restaurant near the hotel they just confirmed. Recent LLM-based approaches infer active domains implicitly from the belief state update without a separate domain classifier module, treating domain detection as part of the joint belief state inference.

• Slot Value Predictor: The central prediction component, operating over the active (domain, slot) pairs identified by the domain classifier. Three prediction sub-strategies have been extensively studied:

  • Ontology-constrained classification (NBT, GLAD, GCE): Selects the predicted value from a fixed candidate list enumerated in the domain ontology (e.g., hotel.area ∈ {north, south, east, west, centre, do not care, none}). Robust to annotation noise and computationally efficient; limited to predefined value sets and cannot handle out-of-vocabulary phrasings (“near the city centre” would fail to match “centre” unless normalised). Best suited for slots with a small, well-defined value set.
  • Span extraction (COMER, DS-DST, BERT-DST): Identifies start and end token positions in the dialogue history that correspond to the slot value, treating DST as a reading comprehension task analogous to SQuAD-style extractive QA. Handles novel and open-vocabulary phrasings naturally, since the prediction is a substring of the dialogue history. Sensitive to ASR errors (a misrecognised value cannot be span-extracted correctly) and requires careful handling of values that are not literally present in the history (e.g., implicit negations or paraphrases).
  • Generative prediction (TRADE, MinTL, SimpleTOD, UBAR): Autoregressively generates slot values token by token using a sequence-to-sequence decoder conditioned on the dialogue history encoder representation. Most flexible approach — can generate any value including complex multi-word descriptions and handle open-vocabulary domains — but requires careful decoding constraints (beam search with vocabulary filtering, constrained decoding) to prevent hallucinated values that are syntactically plausible but semantically incorrect.

• Carry-Over Mechanism: Propagates unchanged slot-value pairs from the previous belief state to the current one, avoiding redundant prediction at each turn for slots whose values have not changed. This is non-trivial because distinguishing genuine carry-over from cases where a prior value should be cleared or corrected requires reasoning over the current utterance. TripPy’s triple copy strategy formalised this as an explicit three-way decision per slot: predict a new value from the current utterance, copy a value from a span in the dialogue history, or carry over the value from the prior belief state. SOM-DST introduced a Selective Overwriting mechanism where the model first classifies each slot as CARRYOVER, UPDATE, or DONTCARE before performing value prediction only for slots flagged as UPDATE.

• Coreference and Ellipsis Resolver: Handles linguistic phenomena that disconnect the surface form of user utterances from their referential content. Examples include anaphoric references (“somewhere near there” referring to a location established in a prior turn), ellipsis (“the same price range as before”), comparative references (“cheaper than that hotel”), and implicit domain switches where a constraint is expressed in domain-neutral terms. These phenomena are systematically underrepresented in automatic annotations and constitute a major source of DST errors on otherwise highly-annotated datasets. Neural models with full dialogue-history attention implicitly learn to resolve many coreferences through the attention mechanism, but systematic ellipsis resolution remains a research challenge.

• Belief State Store: The running accumulation of confirmed (domain, slot, value) triples, updated incrementally at each dialogue turn and representing the system’s current understanding of the user’s goals. This store is the primary interface between the DST module and all downstream pipeline components: the database query module uses it to retrieve entities satisfying all confirmed constraints; the Dialogue Policy module uses it to decide what to do next; and the Natural Language Generation module uses it to contextualise generated responses. In LLM-native architectures, the belief state may be maintained implicitly in the model’s context window or produced explicitly as structured JSON output via function calling at each turn, providing a machine-readable anchor for downstream agentic execution.

  Major Families and Approaches

  DST modelling approaches can be organised along two primary axes: (1) how the value prediction problem is framed (classification, extraction, or generation); and (2) the degree of domain-specificity versus generalisation capability. These axes have traced a trajectory from ontology-bound classifiers requiring complete pre-annotation of all possible values, through span-extractors that operate over open vocabularies from the dialogue text, to LLM-native approaches that treat DST as an in-context language understanding problem requiring no domain-specific parameter updates.

• Ontology-Based Classifiers (NBT, GLAD, GCE, Dialogic): These approaches enumerate all possible slot values in a predefined ontology and score each candidate against the dialogue context using a similarity-based or classification head. The Neural Belief Tracker (NBT; Mrkšić et al., 2017) used pre-trained word vectors to compute semantic similarity between user utterance representations and candidate slot-value pair representations, enabling the first portable DST model that did not require domain-specific feature engineering. GLAD (Global-Locally Self-Attentive DST; Zhong et al., 2018) extended this with a hierarchical attention mechanism separating global (cross-domain) and local (domain-specific) features. Ontology-based approaches are fast at inference time, produce interpretable confidence scores over the candidate value set, and are robust to annotation noise. However, they are fundamentally limited to values that appear in the pre-enumerated ontology — any value not in the list cannot be predicted, making them inapplicable to open-vocabulary slots like hotel name, taxi destination address, or free-text preference descriptions.

• Span-Extraction Models (COMER, SOM-DST, DS-DST, BERT-DST): These reframe DST as a reading comprehension or extractive question answering task, where the model identifies the token span in the dialogue history that corresponds to the slot value, rather than selecting from a fixed candidate list. DS-DST (Zhang et al., 2019) introduced a two-stage approach that routes each slot to either a span extraction head or a classification head based on whether the slot’s domain is open-vocabulary or closed. SOM-DST (Kim et al., 2019) combined selective overwriting — classifying each slot as CARRYOVER, UPDATE, or DONTCARE at each turn — with span extraction for slots flagged as UPDATE. This explicit state operation modelling reduces the number of slot predictions made per turn, substantially improving inference efficiency. Span-extraction models handle novel value phrasings well (the value appears verbatim in the dialogue history) but are sensitive to ASR errors (misrecognised values cannot be extracted) and fail when the slot value is expressed implicitly or through inference rather than directly stated.

• Generative Models (TRADE, MinTL, SimpleTOD, UBAR, PPTOD): Autoregressively generate slot values token by token using an encoder-decoder architecture, conditioned on the full dialogue history. TRADE (Wu et al., 2019) introduced a copy mechanism that allows the decoder to copy tokens directly from the dialogue history — combining the flexibility of generation with the precision of span extraction. The copy mechanism enables TRADE to handle both open-vocabulary slot values and compositional phrasings while maintaining transferability across domains. SimpleTOD (Hosseini-Asl et al., 2020) treated the entire task-oriented dialogue pipeline — DST, database query, dialogue act, and response generation — as a single language modelling task over a linearised sequence, training GPT-2 to autoregressively produce a belief state string followed by all downstream pipeline outputs. This end-to-end approach achieved competitive DST performance while eliminating the modular training complexity of earlier pipeline systems.

• In-Context LLM Methods (IC-DST, FnCTOD, OrchestraLLM, IntentDST): These leverage the few-shot and zero-shot capabilities of large pre-trained language models to perform DST without fine-tuning on dialogue-specific data. IC-DST (Hu et al., 2022, EMNLP) demonstrated that retrieving relevant few-shot dialogue examples and placing them in the LLM’s context window enables competitive DST performance with ChatGPT without any gradient updates. FnCTOD (Feng et al., 2024, ACL) advanced this by representing entire dialogue domains as typed JSON function schemas in the LLM system prompt — encoding slot names, types, descriptions, and example values as function parameters — enabling zero-shot DST where the LLM emits structured function calls encoding the current belief state. This approach achieved 57.1% JGA on MultiWOZ 2.2 with GPT-4 and demonstrated that 7B open-source models (Mistral, LLaMA-2) fine-tuned with function-schema alignment can surpass larger commercial models. OrchestraLLM (Guo & Klakow, 2023) addressed efficiency by routing DST sub-tasks (domain detection, closed-slot classification, open-slot generation) to LLMs of appropriate scale, reducing API costs while maintaining accuracy. Intent-driven in-context learning (He et al., 2024) improved example retrieval for few-shot DST by using the detected user intent as the retrieval key, finding examples with similar intent-belief state pairs rather than surface-level utterance similarity.

• Graph-Enhanced Models (KA-GPT2, NoEticGraph, DialogueKG): These approaches augment standard encoder-decoder architectures with explicit graph representations of dialogue entity relationships, domain ontology structure, or knowledge base connections. Knowledge-Aware Graph-Enhanced GPT-2 (KA-GPT2, Wu et al., 2021) combined GPT-2 generation with a domain-ontology graph encoder that provided structured knowledge about slot interdependencies and domain relationships as additional context during belief state prediction. The noetic graph approach (Wang & Zhang, 2025, Pattern Recognition) used DialoGPT encoders combined with graph attention networks over dialogue entity graphs, achieving a 1.7% improvement across all SGD domains and 0.8% on previously unseen SGD domains, demonstrating that graph-structured domain knowledge can partially compensate for the absence of domain-specific training data.

• Interpretable LLM Summarisation (2025): An emerging approach that generates a natural-language dialogue summary at each turn as an intermediate representation, then extracts the formal belief state from the summary via a second LLM pass. The intermediate summary — describing the conversation’s goal and current status in plain English — is human-readable and auditable, improving system debuggability significantly compared to black-box end-to-end approaches. The summarisation step also appears to improve domain transfer robustness by abstracting away domain-specific surface phrasings into semantically clearer representations. Li et al. (arXiv:2503.08857, 2025) demonstrated that this two-stage pipeline outperforms direct belief state extraction on challenging cross-domain evaluation splits of MultiWOZ, at the cost of two LLM inference passes per turn.

  Relationship to Dialogue Policy and End-to-End Systems

  DST does not operate in isolation — its output is consumed by downstream components that must act on the belief state representation. Understanding the interface between DST and these consumers is essential for system integration:

• DST → Dialogue Policy: The Dialogue Policy receives the current belief state b_t and selects the next system dialogue act (request missing slot, confirm high-uncertainty slot, query database, make offer, confirm booking). In classical POMDP-based systems, the policy is a learned Q-function over (belief state, action) pairs. In LLM-based systems, the policy is the LLM’s next-turn response generation, implicitly conditioned on the belief state as represented in the context window.

• DST → Database Query: The belief state is directly translated into a structured database query. For MultiWOZ hotel, this means: SELECT * FROM hotels WHERE area = belief.hotel.area AND pricerange = belief.hotel.pricerange AND parking = belief.hotel.parking ORDER BY stars DESC. The number of returned entities (0, 1, or many) conditions the policy’s next action.

• DST → Natural Language Generation: In NLG-conditioned systems, the current belief state is provided to the NLG module to ensure generated responses correctly reference confirmed constraints and avoid contradicting the established state.

• End-to-End Bypass: In end-to-end systems (SimpleTOD, UBAR, LLM-native), the explicit DST module is bypassed — the system implicitly maintains belief state in its neural representations without producing an interpretable intermediate belief state object. This improves system simplicity but reduces auditability, making it harder to debug why the system made a particular database query or offered a particular entity.

• Explicit vs. Implicit DST: The choice between explicit DST (producing a structured belief state object consumed by downstream components) and implicit DST (end-to-end modelling without explicit intermediate state) is a key architectural decision with implications for interpretability, modularity, and the ability to inject human corrections.

• DST in Agentic Architectures: In Tool-Augmented Reasoning dialogue systems, the belief state functions as a structured parameter store for downstream API calls — the (domain, slot, value) triples translate directly into function call arguments. This tight coupling between DST and tool execution makes DST accuracy directly consequential for task completion success in agentic settings.

  Formal Procedure: DST at Each Dialogue Turn

  The canonical execution procedure for a pipeline DST module at dialogue turn t is:

• Step 1 — Encode Dialogue History: Concatenate the full sequence of alternating system (S) and user (U) utterances [S_1, U_1, S_2, U_2, …, S_{t-1}, U_t] with special separator tokens marking turn and speaker boundaries. Pass through a pre-trained transformer encoder (BERT, T5 encoder, or LLM context) to produce contextualised token representations h_{1:T}.

• Step 2 — Detect Active Domains: Apply a domain classifier over the full history representation to predict the set of currently active dialogue domains D_t ⊆ D. For efficiency, many systems skip prediction for slots in domains detected as inactive, avoiding unnecessary inference over the full (domain, slot) Cartesian product.

• Step 3 — Classify Slot Operation: For each (domain, slot) pair (d, s) in active domains D_t, classify the slot operation type: CARRYOVER (value unchanged from t-1), UPDATE (new value in U_t), DONTCARE (user expressed no constraint), or NONE (slot not yet mentioned). Only slots with operation UPDATE require value prediction in step 4.

• Step 4 — Predict Slot Values: For each slot flagged as UPDATE, apply the appropriate prediction head:

  • Categorical slots: score each candidate value in V_{d,s} against h_{1:T} and select the highest-scoring value
  • Open-vocabulary / extractive slots: identify the token span [start, end] in h_{1:T} maximising P(start) × P(end)
  • Generative slots: autoregressively decode a value string from a conditional language model head given h_{1:T}

• Step 5 — Apply Carry-Over: For all slots not flagged as UPDATE, carry over the value from b_{t-1}(d, s) into b_t(d, s). For slots flagged as DONTCARE, set b_t(d, s) = “dontcare”. For slots flagged as NONE and not previously active, leave b_t(d, s) = “none”.

• Step 6 — Produce Updated Belief State: Assemble the complete belief state b_t = {(d, s, v) : v = predicted value or carried-over value, for all (d, s) in all active domains D_t}. This set of triples is the output passed to the database query and Dialogue Policy modules.

• Step 7 (LLM variant) — Function Call Emission: In LLM-based zero-shot DST (FnCTOD approach), instead of the above module pipeline, the LLM is provided with domain schemas as JSON function signatures in the system prompt and produces a structured function call output directly encoding the complete updated belief state as arguments to the relevant domain function schemas, bypassing steps 1-6 as separate modules.

  Formal Representation

  A belief state b_t at turn t over a multi-domain ontology Ω = {(d, s, v) : d ∈ D, s ∈ S_d, v ∈ V_{d,s}} is a probability distribution over all valid state configurations. Under simplifying independence assumptions (factorised belief state), the joint distribution decomposes as:

  b_t(Ω) ≈ ∏_{d∈D} ∏_{s∈S_d} P(v_{d,s,t} | history_{1:t})

  Joint Goal Accuracy (JGA) at turn t is:

  JGA_t = 1[∀(d,s): ŷ_{d,s,t} = y*_{d,s,t}]

  where ŷ is the predicted value and y* is the gold-standard annotation. The strict equality requirement means JGA is a very conservative metric — a single mispredicted slot in any active domain at any turn counts as a failure for that turn.

  Benchmark Datasets

• MultiWOZ 2.0 / 2.1 / 2.2 / 2.4: The de facto DST benchmark and the most widely cited multi-domain dialogue dataset in the research literature. MultiWOZ contains 10,438 annotated goal-oriented dialogues collected via Wizard-of-Oz methodology, spanning 7 domains (hotel, restaurant, train, taxi, attraction, hospital, police), 35 slot types, and averaging 13.7 turns per dialogue. Annotation quality has been iteratively improved through versions 2.1 (2020, correcting 32.6% of annotations in the original 2.0), 2.2 (2020, further correcting 17.3% of utterances on top of 2.1), and 2.4 (2022, applying the most comprehensive annotation audit to date). Joint Goal Accuracy (JGA) on MultiWOZ 2.1 reached 50.91% with multi-level neural belief tracking systems as of 2024; state-of-the-art LLM-based zero-shot systems (FnCTOD, ACL 2024) achieved 57.1% JGA on MultiWOZ 2.2, with smaller open-source models (Mistral-7B) surpassing ChatGPT on this metric through function-schema prompting.

• Schema-Guided Dialogue (SGD): Google’s large-scale TOD benchmark specifically designed to test generalisation to unseen slot schemas. SGD contains 16,142 dialogues across 20 domains (banking, buses, calendar, events, flights, homes, hotels, media, messaging, movies, music, renters, restaurants, ridesharing, services, shopping, sports, trains, travel, weather) with explicit natural language schema descriptions that vary in phrasing across train and test splits. The explicit separation between slot schema descriptions and dialogue data enables zero-shot schema generalisation evaluation — testing whether a system trained on some schemas can track dialogue state for previously unseen schemas using only their natural language descriptions. Graph-attention-network approaches (Wang & Zhang, 2025) achieved a 0.8% absolute improvement on unknown-domain slots and 1.7% overall on SGD compared to prior SOTA.

• ATIS (Airline Travel Information System): The classic single-domain slot filling benchmark, containing transcriptions of passengers querying a flight information system across a constrained vocabulary of flight-related slots (origin, destination, departure date, arrival time, airline, flight class). ATIS was the standard slot-filling benchmark from the 1990s through approximately 2018; it is now largely superseded by multi-domain benchmarks for DST evaluation but continues to be used for ablation studies, encoder pre-training evaluation, and as a controlled single-domain comparison point for multi-domain transfer experiments.

• M2M (Machines Talking to Machines; Shah et al., 2018): A simulation-based dataset generated by having two dialogue agents (acting as user and system respectively) converse in a restricted language, producing structured dialogue transcripts across 2 domains with controllable variation. M2M was designed specifically to support the study of data-efficient transfer to new domains with minimal human annotation, making it relevant to the continual learning and zero-shot generalisation problems that dominate contemporary DST research.

• DialoGLUE (2020): A suite of seven NLU benchmarks for task-oriented dialogue, including slot filling and intent detection tasks drawn from multiple domains, designed for pre-training evaluation and few-shot generalisation assessment.

• ToolDial (ICLR 2025): A multi-turn dialogue dataset explicitly framing DST as a multi-turn tool-use inference problem, where each dialogue turn involves selecting and parametrising API calls based on the accumulated conversational context. ToolDial connects dialogue state tracking to the emerging Tool-Augmented Reasoning paradigm, enabling evaluation of whether DST models can track state in agentic settings where the belief state directly drives executable API invocations rather than passive database queries.

  Use Cases

• Hotel and Travel Booking: The canonical DST use case and the primary domain in MultiWOZ — tracking destination city, check-in date, check-out date, price range, star rating, amenities (parking, wifi), area (north/south/centre), and number of guests across a multi-turn dialogue that may involve clarification, correction (“actually three nights, not two”), and mid-dialogue preference updates. DST enables the system to submit a database query encoding all confirmed constraints without the user needing to repeat them, and to present matching options at the correct level of specificity.

• Restaurant Recommendation: Maintaining cuisine type, price band, geographic area, party size, and booking time across correction and refinement turns, with carry-over between related bookings (restaurant near the hotel the user just selected). Cambridge’s DSTC 2 challenge (Henderson et al., 2014) used restaurant booking as the primary test domain, establishing the classic slot vocabulary (food, pricerange, area, name, phone, postcode) that persists in subsequent benchmark designs.

• Train and Transit Booking: Tracking departure location, destination, travel date, arrival time constraint, and number of ticket slots across dialogues that often involve presenting alternatives when the originally requested service is unavailable, requiring the DST system to distinguish between constraints the user is willing to relax and those that are firm requirements. MultiWOZ’s train domain is particularly challenging for DST because time constraints (arrive by, leave after) interact with the retrieved database in ways that require the policy to reopen negotiation.

• Customer Support and Service Desk Automation: Tracking account identifiers, product model numbers, issue descriptions, attempted resolution steps, customer tier, and escalation status across a support conversation. Effective DST in this domain prevents the user from repeating information when transferred between support agents or between automated and human handling, addressing one of the primary friction points in legacy IVR systems. UK telecoms operators (BT, O2, Virgin Media) and banks deploy slot-tracking components within their customer service dialogue platforms.

• Healthcare Symptom Collection and Clinical Intake: In clinical dialogue systems, DST tracks symptom onset date, symptom severity (rated 1-10), medication history, allergy flags, comorbidities, and presenting complaint across a structured clinical intake conversation. The belief state output feeds directly into a clinical decision support module that matches the slot-value representation against condition ontologies. NHS-facing systems like those evaluated under the NHS AI Lab’s clinical dialogue programme require DST outputs to meet accuracy thresholds sufficient for safe clinical triage.

• Voice-Activated Smart Home and IoT Control: Tracking device references (often through spatial deictics — “the lights in there”), brightness or temperature preferences expressed in natural language (“a bit warmer than it is now”), mode selections, scheduling constraints, and device group membership across multi-turn voice commands where ASR errors on device names and room references are common. The main DST challenge in this domain is resolving vague references to devices, rooms, and settings through a spatial and semantic context maintained in the belief state.

• Intelligent Tutoring and Education: Tracking student-declared knowledge gaps (“I don’t understand integration by parts”), attempted question types (multiple choice, worked example, open-ended), error patterns accumulated across the session, preferred explanation level (formal/informal, visual/textual), and curriculum progress. The belief state in an educational dialogue system must represent not just what the student has said but inferred learning state — combining stated preferences with system-observed performance — to drive personalised content delivery.

• Financial Services Dialogue: Tracking account type, transaction amount ranges, date constraints, merchant categories, fraud dispute details, and product preferences across dialogues for account management, loan applications, and investment enquiries. GDPR and financial regulation requirements in the UK mean that DST systems in regulated financial dialogue must maintain an auditable log of the belief state at each turn, making interpretable DST architectures particularly attractive for this sector.

  Academic Context

  The Dialogue State Tracking Challenges (DSTC) series, inaugurated by Jason Williams (then at AT&T Research) and Steve Young (Cambridge) in 2013, has been the primary coordinating mechanism for DST research progress across academic and industrial institutions. DSTC 1 (2013) used a bus information corpus; DSTC 2 and 3 (2014-2015) introduced restaurant booking and tourist information domains with a more diverse candidate value vocabulary; DSTC 4 addressed crowdsourced dialogue; DSTC 5 extended evaluation to cross-lingual transfer scenarios (English to Chinese); DSTC 6 and 7 tackled end-to-end learning; DSTC 8 (2019) and DSTC 9 (2020-2021) incorporated Schema-Guided Dialogue and open-domain constraints alongside task-oriented settings; DSTC 10 and 11 (2022-2024) addressed knowledge-grounded and subjective-knowledge dialogue scenarios. The DSTC series has produced the standard benchmarks and enabled systematic comparison of modelling approaches across institutions.

  The Cambridge Machine Intelligence Laboratory (MIL), under Steve Young, pioneered the Bayesian Update of Dialogue State (BUDS) and Hidden Information State (HIS) frameworks that underpinned competitive systems from 2010-2016 and established the conceptual vocabulary of the field. Phil Woodland and Tony Robinson contributed to the ASR foundations enabling early Cambridge spoken dialogue systems. Milica Gašić (Cambridge, now Heinrich Heine University Düsseldorf) led neural extensions of POMDP dialogue management and co-authored TRADE and TripPy. Edinburgh’s ILCC (Institute for Language, Cognition and Computation) has contributed to spoken language understanding, cross-lingual DST, and speech-integrated tracking. The Oxford Applied and Theoretical Machine Learning (OATML) group has produced work on uncertainty-calibrated dialogue policy learning that critically depends on well-calibrated DST belief state distributions. Queen Mary University London has contributed to DST under noisy conditions and to dialogue simulation for data augmentation.

  In industry, Amazon Alexa Science has published extensively on scalable DST for production voice systems, addressing multi-intent handling, value normalisation, and slot carry-over at conversational scale across dozens of skill domains. Google Research produced the Schema-Guided Dialogue (SGD) benchmark and associated DST models specifically designed for generalisation to new schemas without requiring full dataset re-annotation — a practical necessity for enterprise dialogue systems that must be extended to new business domains rapidly. Salesforce Research contributed SimpleTOD and the TOD-BERT pre-training approach. Microsoft Research has produced numerous DST systems through the DSTC challenges, including the SUMBT (Slot-Utterance Matching Belief Tracker) architecture (Lee et al., 2019) that combined BERT utterance encodings with memory networks for efficient multi-domain tracking. Meta AI Research (FAIR) has contributed to DST through the BlenderBot and OPT dialogue research programmes. The SIGdial workshop (annual, co-located with ACL or EMNLP) and the InterSpeech conference are the primary academic venues for DST work; the ACL Anthology maintains a comprehensive archive of peer-reviewed DST papers spanning 2013-2026.

  Current Landscape (2026)

  By 2026, the dominant production DST paradigm in enterprise applications has shifted from fine-tuned specialist DST models to LLM-native belief state inference operating over large context windows. In high-throughput customer service deployments where latency and cost are critical, lightweight specialist encoders (TripPy-class or TRADE-class models, 110M-350M parameters) are still preferred for their predictable latency profiles. For complex, long-horizon conversations or scenarios requiring zero-shot generalisation to new domains (as in rapid enterprise deployment), LLM-based DST via function calling or structured prompting has become the practical default.

  The FnCTOD paper (ACL 2024) demonstrated that representing dialogue domains as JSON-formatted function schemas within the LLM system prompt enables 7B-parameter open-source models (Mistral, LLaMA-2) to surpass ChatGPT’s zero-shot DST performance — a significant finding for deployments requiring data privacy or on-premises execution. Research in 2025 has focused on interpretable DST via natural language summarisation, where the LLM generates a human-readable summary of conversation progress as an intermediate step before extracting the formal belief state, improving both debugging and domain transfer robustness. Graph-enhanced approaches using noetic dialogue graphs with graph attention networks have shown particular strength on the Schema-Guided Dialogue benchmark, especially for low-resource domains. The emerging ToolDial paradigm (ICLR 2025) frames DST as a multi-turn tool-calling problem, connecting dialogue state inference to Tool-Augmented Reasoning architectures and enabling tighter integration between DST and agentic downstream execution.

  Key open challenges as of 2026 include: (1) handling very long dialogue histories (50+ turns) where positional encoding and attention context limitations degrade performance; (2) robust DST in noisy ASR conditions typical of real-world voice deployments; (3) cross-lingual and low-resource DST, particularly for non-English languages underrepresented in MultiWOZ and SGD; (4) continual learning of new slot schemas without catastrophic forgetting of previously learned domains.

  UK Context

  Cambridge University’s Dialogue Systems Group, led by Steve Young (now emeritus), produced foundational DST research spanning 2008-2018 that remains canonical: the BUDS framework, HIS model, DSTC challenge series co-organisation, and the POMDP-based dialogue management systems that won multiple DSTC evaluations. Cambridge’s contributions have been cited thousands of times and directly seeded DST research at Edinburgh, Sheffield, and Imperial. The Engineering and Physical Sciences Research Council (EPSRC) funded multiple Cambridge dialogue systems projects, including the PARLANCE project on adaptive spoken dialogue.

  Edinburgh’s ILCC (Institute for Language, Cognition and Computation) has contributed to multilingual dialogue state tracking and speech-integrated DST systems, leveraging the university’s strengths in Automatic Speech Recognition and low-resource language processing. The Edinburgh-Cambridge collaboration through the EPSRC Centre for Doctoral Training in Natural Language Processing has produced multiple PhD cohorts working on dialogue understanding.

  In industry, the UK’s BT Research and Innovation division has applied DST within its customer service automation programmes. Babylon Health (now acquired) and its successors built clinical dialogue systems with DST components for structured symptom collection in NHS-adjacent deployments. EBO AI, a UK-based company directly integrated into NHS App workflows as of 2026, deploys multilingual conversational AI with DST for patient-facing appointment management and service navigation across NHS Trusts. The NHS’s AI Lab and NHSX have evaluated dialogue state tracking architectures for structured clinical intake conversations, particularly for mental health triage in the DIALOG+ programme implemented across multiple NHS Trusts (East London, Hampshire, North London).

  In Northern England, Sheffield’s NLP research group (University of Sheffield, part of the GATE NLP platform ecosystem) has contributed to entity recognition and slot filling components applicable to DST. Manchester Digital, the trade body representing Greater Manchester’s digital sector, has positioned conversational AI including DST-enabled systems as a priority technology for the city’s financial services and logistics sectors, with the Co-op (headquartered in Manchester) and First Group deploying customer-service dialogue systems with explicit slot-tracking components.

  Training and Data Collection

  DST systems require labelled dialogue corpora in which each turn is annotated with the complete belief state at that point in the conversation. Producing such corpora is expensive: annotation requires annotators to read the full dialogue history up to each turn and produce a complete (domain, slot, value) triple set — a cognitively demanding task that has been documented to produce substantial inter-annotator disagreement and systematic errors, as evidenced by the successive annotation correction rounds in MultiWOZ 2.1-2.4. The annotation cost has driven research into three alternative data collection strategies:

• Wizard-of-Oz Collection: Human “wizards” respond to user queries while the researcher simultaneously annotates the belief state from the wizard’s perspective. This is the methodology used for MultiWOZ (10,438 dialogues collected via WOZ with a database-backed wizard interface). The wizard interface’s automatic logging of database queries and selected entities provides a partial ground-truth annotation that is then manually verified and corrected. WOZ collection is expensive (approximately £5-15 per dialogue including annotation), making large-scale multi-domain collection a significant research resource investment.

• Simulation-Based Data Augmentation: User simulators that generate synthetic dialogue turns according to a parameterised user goal model are used to generate additional training data at scale. The M2M (Machines Talking to Machines) dataset used simulation for initial data generation, with human paraphrase to naturalise the language. User simulators (ABUS, ConvLab user models) enable generating thousands of dialogues for new domains without WOZ collection costs, but simulated language is typically less naturalistic than human WOZ data, and domain coverage depends on the quality of the user goal model.

• LLM-Based Data Synthesis: Large language models are increasingly used to generate synthetic dialogue data with automatic belief state annotation, using prompting to specify the user goal and domain schema. LLM-synthesised TOD data can be generated at very low cost and in multiple languages, but requires careful quality validation (hallucinated slot values, inconsistent belief state updates, and unrealistic dialogue flow patterns are common failure modes). LLM-driven multi-turn dialogue synthesis for realistic reasoning (arXiv:2602.23610, 2025) is an emerging approach to generating high-quality synthetic TOD training data.

• Self-Supervised Pre-Training (TOD-BERT): Rather than relying exclusively on labelled DST data, TOD-BERT (Wu et al., 2020) pre-trained a BERT model on large unlabelled TOD corpora using response selection and next utterance prediction as self-supervised objectives, producing a dialogue-specialised encoder that significantly improved downstream DST fine-tuning efficiency on MultiWOZ.

  Cross-Domain Transfer in DST

  A central practical challenge for deploying DST in enterprise contexts is domain portability — the ability to use a DST system trained on annotated data in one set of domains (e.g., hotel and restaurant in English) to accurately track dialogue state in a new domain (e.g., insurance, electronics retail, UK GP surgery booking) without collecting and annotating large new training datasets.

  Several strategies for cross-domain DST transfer have been explored:

• Schema-Driven Transfer: Systems like TRADE use a shared vocabulary across domains through a copy mechanism that generates slot values by copying from the dialogue history — enabling the model to handle slot values from any domain as long as they appear literally in the user’s utterances. Schema-driven prompting (Vulic et al., EMNLP 2021) provides domain schema descriptions in natural language as part of the model input, enabling BERT-based models to generalise across schema variants without retraining.

• Zero-Shot Function Schema (FnCTOD): Domains are represented as typed JSON function schemas embedded in the LLM system prompt. At inference time, the LLM receives the dialogue history and the function schemas for all active domains, and produces function call outputs encoding the current belief state. New domains can be added by defining their schemas — no retraining required. Demonstrated to achieve competitive zero-shot performance on MultiWOZ 2.2 domains without any dialogue-specific fine-tuning.

• Meta-Learning for DST: Few-shot learning approaches (MAML-based, prototypical network-based) are designed to enable rapid adaptation to new domains from as few as 5-20 annotated examples. MetaDST and related models learn a parameter initialisation that can be efficiently fine-tuned to new domains with minimal data. Practically important for enterprise deployments where business-specific domains have limited annotation budgets.

• Data Augmentation via LLM Synthesis: New domain training data is synthesised by prompting large language models to generate realistic dialogue transcripts for the target domain, with automatic belief state annotation derived from the prompt-specified user goal parameters. This approach has been validated for generating MultiWOZ-style dialogues in new domains; quality validation against human-annotated data is still required for production deployment but substantially reduces annotation costs.

• Cross-Lingual Transfer via mBERT / XLM-R: Multilingual pre-trained encoders trained on 100+ languages enable DST models trained on English MultiWOZ to transfer to other languages by exploiting cross-lingual semantic alignment in the encoder representation space. Zero-shot cross-lingual DST (English train, German/Chinese/French test) shows substantially lower JGA than English evaluation but is improving rapidly with multilingual instruction-tuned LLMs (mT5, Llama-multilingual).

• Ontology Expansion without Retraining: Some DST architectures (open-vocabulary models, LLM-based approaches) can handle new slot values — including values entirely absent from training data — through their generative or extractive capabilities. This enables ontology expansion (adding new values to existing slot vocabularies) without retraining, a practical requirement in dynamic enterprise domains where new product names, service tiers, and location names are constantly added.

  Error Analysis and Failure Modes

  Understanding where and why DST systems fail is essential for targeted improvement. The primary error categories documented across MultiWOZ evaluation studies are:

• Annotation Errors in Ground Truth: A significant fraction of MultiWOZ “failures” represent disagreements between the model’s reasonable interpretation and flawed ground-truth annotation. MultiWOZ 2.4 identified and corrected 5,165 annotations that were objectively incorrect; studies comparing model outputs against the uncorrected 2.0 annotations find that many “errors” are actually correct system outputs being evaluated against wrong gold labels. This underscores the importance of reporting across multiple dataset versions.

• Coreference and Ellipsis Errors: The single largest source of genuine model errors. References to previously-mentioned entities (“the same place”, “that one we discussed”, “near there”) require the model to perform accurate coreference resolution over the full dialogue history — a capability that transformer attention mechanisms partially provide but do not fully solve, particularly for long histories or indirect references spanning multiple turns.

• Implicit Value Modifications: Users often modify slot values without explicitly flagging the modification (“I also need parking” effectively modifying hotel.parking from “none” to “yes”; “actually make it for five nights” correcting hotel.duration without explicitly mentioning the original value). Models trained primarily to recognise explicit inform acts systematically under-detect these implicit modifications.

• Cross-Domain Carry-Over Errors: When a user’s request in one domain implies a constraint on another domain (the restaurant booking date implicitly equalling the hotel check-out date), DST systems must infer the implicit carry-over without explicit user confirmation. Multi-domain carry-over accounts for a disproportionate share of JGA failures on MultiWOZ relative to its frequency.

• Numerical and Temporal Normalisation: Slot values expressed as relative references (“next Friday”, “around lunchtime”, “about 30 minutes from the centre”) require normalisation to canonical forms (date = 2026-06-27, time = 13:00, area = centre) that may not be directly present in the dialogue text. These normalisation failures are especially common for time-related slots and distance/location constraints.

• ASR-Induced Errors in Voice Deployments: In live speech dialogue deployments (as opposed to text-based MultiWOZ evaluation), ASR errors on key slot values (name misrecognition, number confusion, accent-induced errors) propagate into DST and cause belief state corruption. Studies of production voice dialogue systems find that ASR error rate correlates strongly with DST accuracy degradation, particularly for proper noun slots (hotel name, restaurant name, street name).

  Evaluation Metrics and Protocols

  DST evaluation has evolved alongside the benchmark datasets, with increasing attention to the gap between automatic metrics and deployment-relevant quality. The key metrics and their properties are:

• Joint Goal Accuracy (JGA): The primary DST metric. At each dialogue turn, JGA is 1 if and only if all predicted (domain, slot, value) triples exactly match the gold-standard annotation for all active domains simultaneously. JGA is a very conservative binary success metric — a single mispredicted slot in any active domain at any turn scores as a complete failure. This strictness is intentional: for a downstream database query, any incorrect slot value will produce wrong entity retrieval regardless of how many slots are correct. JGA on MultiWOZ 2.1 has improved from approximately 25% (early neural systems, 2018) to 57%+ (LLM-based zero-shot, 2024).

• Slot Accuracy: A more lenient metric that computes the fraction of individual (domain, slot) pairs correctly predicted, averaging across all active slot-domain pairs across the dialogue. Slot accuracy provides a more granular picture of system performance — a system with 90% slot accuracy but 30% JGA may be quite usable in practice if the errors are concentrated in low-priority or rarely-active slots.

• Inform Rate and Success Rate: End-to-end task completion metrics used in conjunction with DST. Inform rate measures whether the system provided all information the user needed (is the retrieved entity the correct one?); success rate measures whether the user’s stated task goal was ultimately satisfied. These metrics require evaluating the full dialogue pipeline, not just the DST module, and are computed over complete dialogues rather than individual turns.

• Schema F1 and Slot F1: For zero-shot and schema generalisation evaluations (SGD), F1 scores over active slot-domain pairs provide a balanced precision-recall metric that better captures performance on sparse belief states (where most slots are inactive at any given turn).

• Human Evaluation: Increasingly used to complement automatic metrics, particularly for assessing whether DST errors have perceptible impact on dialogue quality. Human judges may rate overall dialogue coherence, task completion, and error recovery — providing a more ecologically valid quality signal than JGA but requiring expensive human annotation.

• Annotation Consistency Analysis: As annotation quality variability across MultiWOZ versions has been documented (17-32% corrections per version upgrade), research increasingly reports results across multiple dataset versions (2.1, 2.2, 2.4) to characterise sensitivity to annotation noise.

  Dialogue Act Representation in DST

  DST interfaces closely with dialogue act recognition and generation — the formal representation of what communicative function each utterance is performing. The standard dialogue act taxonomy for task-oriented dialogue (adopted from DM2S and ISO 24617-2) includes:

• Inform(domain, slot, value): The user provides a constraint or value (e.g., “I want a cheap hotel in the north” → Inform(hotel, pricerange, cheap), Inform(hotel, area, north)).

• Request(domain, slot): The user requests information about a slot value (e.g., “What is the phone number?” → Request(hotel, phone)).

• Confirm(domain, slot, value): The user confirms a previously stated value (e.g., “Yes, that’s right, Cambridge” → Confirm(hotel, area, cambridge)).

• Deny(domain, slot, value): The user denies or corrects a previously assumed value (e.g., “No, I said cheap, not expensive” → Deny(hotel, pricerange, expensive)).

• DontCare(domain, slot): The user expresses that a slot value is not a constraint (e.g., “Any area is fine” → DontCare(hotel, area)).

• NoOffer(): The system informs the user that no entities match the current belief state constraints.

• Offer(domain, name): The system proposes a specific entity matching the belief state constraints.

• Book(domain): The system confirms a booking for an offered entity.

  The relationship between dialogue acts and belief state updates is central to DST: the belief state is computed by accumulating Inform and Confirm acts and retracting or overwriting values when Deny or correction acts are detected. The ability of a DST system to correctly identify implicit dialogue acts — particularly DontCare and implicit Deny — is a primary differentiator between strong and weak systems on MultiWOZ.

  Future Directions (2026-2030)

• Zero-Shot Domain Generalisation: DST systems that can infer the slot schema for a previously unseen domain from natural language domain descriptions alone, without any annotated dialogue examples for that domain. LLM-based function calling approaches (FnCTOD) have demonstrated this is achievable in principle, with 7B open-source models achieving competitive zero-shot JGA on MultiWOZ 2.2; production-grade robustness across the arbitrary diversity of real-world business domains (legal, real estate, insurance, manufacturing, logistics) remains an open problem requiring further schema description standardisation and cross-domain evaluation benchmarks beyond SGD.

• Incremental and Streaming DST: Current systems update belief state only at end-of-utterance boundaries, processing each complete turn as a unit. Incremental DST — updating slot value predictions word by word as the utterance is being spoken — is essential for low-latency voice assistants that need to begin database queries and response preparation before the user finishes speaking, and enables early interruption and barge-in when the system detects a high-confidence belief state update from a partial utterance. This requires ASR streaming integration and incremental decoder architectures.

• Cross-Lingual and Multilingual DST: Extending DST robustness across the full spectrum of languages rather than primarily English. Transfer from English-annotated MultiWOZ to under-resourced languages via cross-lingual pre-training (mBERT, XLM-R, mT5) has been explored but remains far below English performance in most settings. The practical urgency is high: the UK’s multilingual NHS patient population includes speakers of Bengali, Gujarati, Punjabi, Polish, Somali, and many other languages for whom English-only dialogue systems represent an accessibility barrier.

• Long-Horizon Session Tracking: Multi-session DST that maintains belief state and user preference information across separated dialogue sessions — capturing prior booking confirmations, expressed preferences, household member profiles, and prior resolution history from interactions that may have occurred days or weeks earlier. This is architecturally connected to Retrieval-Augmented Generation approaches that retrieve prior session summaries at the start of a new conversation, or to persistent user profile stores that accumulate confirmed preferences across sessions.

• Calibrated Uncertainty in Belief State: Production DST systems typically output point-estimate slot value predictions without associated confidence scores. Returning calibrated confidence distributions over possible slot values enables downstream Dialogue Policy components to make explicit decisions about whether to commit to an uncertain slot value, ask a clarifying question, or present alternatives — rather than silently committing to potentially wrong slot values that then propagate as database query errors. Calibrated uncertainty estimation for sequence generation and token-level classification in LLMs is an active research area (conformal prediction, temperature scaling).

• DST for Agentic Task Execution: As Task-Oriented Dialogue Systems evolve into multi-step agentic systems that execute real-world actions through API calls, form submissions, and database modifications, DST must track not just user preferences but also intermediate execution states, partial task completion flags, API call results that should be confirmed with the user, and exception states where an action failed and recovery is required. This expands the belief state from a flat slot-value store to a hierarchical task graph encoding both declarative user goals and imperative execution state — requiring new ontological frameworks and new evaluation protocols beyond JGA.

• Privacy-Preserving and Federated DST: Healthcare and financial service applications require that user preference data and conversational history remain on-device or within regulated data boundaries. Federated learning approaches that train DST models without centralising dialogue data, combined with differential privacy mechanisms that prevent user identification from model updates, are essential enablers for deployment of DST in highly regulated sectors. Edge DST models (1-7B parameter LLMs running on mobile or edge hardware) that perform belief state inference locally without cloud API calls represent the practical direction for the most privacy-sensitive applications.

  Production Deployment Considerations

  Deploying DST in production dialogue systems introduces engineering requirements beyond research benchmark accuracy:

• Latency Constraints: A DST module in a real-time voice dialogue system must complete inference within 50-200ms to maintain conversational naturalness. BERT-based DST modules (110M-350M parameters) on CPU hardware typically require 80-150ms per turn; GPU-accelerated inference reduces this to 10-30ms. LLM-based DST via API (GPT-4, Claude) introduces 300-1500ms API latency per turn, making dedicated, locally-served smaller models (7B-13B LLMs) preferable for latency-sensitive voice applications.

• Multi-Turn State Consistency: As dialogue length increases, DST systems must maintain consistency of the evolving belief state — ensuring that a slot value confirmed three turns ago is not inadvertently overwritten by a new utterance that does not explicitly reference that slot. Production systems typically implement explicit carry-over gating rules that suppress slot updates when the new utterance content is semantically unrelated to the slot in question.

• ASR N-Best Integration: In voice dialogue, the ASR module produces an N-best list of alternative transcriptions with confidence scores rather than a single deterministic output. Robust production DST systems integrate over the N-best list — computing belief state updates for each hypothesis and combining them weighted by ASR confidence — rather than relying on the single top hypothesis, improving robustness to ASR errors on key slot values.

• Schema Update and Domain Expansion: Enterprise dialogue systems frequently require adding new domains or modifying slot vocabularies as business requirements evolve. Fine-tuned specialist DST models require retraining on updated corpora for each schema change; LLM-based function-calling DST enables new domain addition by simply providing new function schemas in the system prompt, making it substantially more agile for rapidly evolving enterprise deployments.

• Graceful Error Recovery: When DST errors produce incorrect belief states that lead to unhelpful system responses, the dialogue system must detect user corrections (“No, I said cheap, not expensive”) and update the belief state accordingly. Correction handling — distinguishing a genuine slot update from a confirmation, and propagating the correction through all downstream components — is a critical production reliability requirement not well-covered by standard JGA evaluation.

• Auditing and Explainability: Regulated sector deployments (healthcare, financial services, legal) require that the belief state derivation at each dialogue turn is auditable — traceable to specific user utterances that supported each slot value inference. Interpretable DST approaches (span extraction with highlighted evidence, LLM summarisation with attribution) are preferable to black-box end-to-end models in these contexts.

• Confidence Thresholding: Production DST systems can be configured to flag low-confidence slot value predictions for explicit user confirmation (“Did you say you wanted a hotel in the centre?”) rather than propagating uncertain values silently into database queries. Calibrated confidence thresholding — setting the confirmation trigger at a confidence level that optimises the trade-off between unnecessary confirmation requests and downstream query errors — requires evaluation on held-out production dialogue logs.

  Known Limitations and Open Challenges

  Despite substantial progress, DST systems as of 2026 exhibit well-documented limitations that constrain production reliability:

• Annotation Noise Sensitivity: DST models trained and evaluated on MultiWOZ inherit biases and errors from the annotation process. The three major annotation revisions (2.0→2.1→2.2→2.4) corrected between 5,165 and 32,000 annotations respectively, significantly changing published JGA rankings — models that appeared SOTA under 2.0 annotations performed worse under 2.4 annotations than their reported scores suggested. This instability makes it difficult to reliably compare systems published in different years.

• Long-Context Degradation: Most DST systems show significantly degraded performance on very long dialogues (15+ turns) due to limitations in how transformer attention handles long sequences and in how carry-over mechanisms handle complex multi-turn state evolution. Production systems in contact centres — where dialogues can span 30-50 turns with multiple topic shifts — encounter challenges not well represented in MultiWOZ evaluation splits.

• Cold-Start for New Domains: Fine-tuned DST models require hundreds to thousands of annotated examples in a new domain to reach reliable performance. While function-calling LLM approaches reduce this to zero-shot inference from schema descriptions, the zero-shot performance gap for complex domains (where slot value sets are large and domain-specific normalisation rules are important) remains significant.

• Numerical and Temporal Parsing: Slots involving numerical values, times, dates, durations, and relative quantities are systematically harder for DST models than categorical or named-entity slots. Expressions like “about 30 minutes from the centre”, “next Friday afternoon”, “under £50 per night”, or “at least 3 stars” require numerical reasoning and temporal anchoring that pure pattern-matching or classification models handle poorly. LLMs handle these better but still make systematic errors on unusual expressions.

• Multi-Intent Utterances: A single user utterance may express constraints across multiple domains or multiple slots simultaneously (“I need a cheap hotel near the train station and a taxi to pick me up at 9”). DST models trained to predict one slot at a time may fail to capture all the constraints expressed in such utterances.

• Annotation Coverage Bias: MultiWOZ covers 7 English-language service domains in a British English register. DST models trained on this data show poor generalisation to non-English languages, non-British cultural contexts (different service conventions, different constraint vocabulary), and to domains not represented in the benchmark (healthcare, financial services, legal, retail returns) without explicit domain adaptation.

• Feedback Loop with Policy Errors: In deployed dialogue systems, DST errors propagate to database queries and policy decisions, which produce incorrect system responses, which in turn generate user correction utterances. The correction language produced by real users in response to system errors has different distributional characteristics from the proactive dialogue turns in training data, creating a distribution mismatch that degrades DST correction-handling in production.

  Relationship to Adjacent NLP Tasks

  DST shares methodology with several adjacent Natural Language Processing tasks but is distinct from each in key ways:

• Compared to Named Entity Recognition (NER): NER identifies and classifies named entities (person, organisation, location, date) in a single document or sentence. DST is similar in that it identifies slot values — which often correspond to named entities — from dialogue utterances. However, DST must additionally track which domain the entity belongs to, how it relates to prior constraints, and whether it represents a new constraint or a correction of an earlier one. DST across a multi-turn dialogue thus requires discourse-level reasoning that standard NER applied sentence-by-sentence cannot provide.

• Compared to Information Extraction (IE): IE extracts structured information from unstructured text, typically operating over documents rather than conversations. DST can be seen as an incremental IE task applied to a dynamically growing dialogue context, with the additional complexity that later utterances may modify, correct, or contextualise information established in earlier utterances. The carry-over and correction phenomena in DST have no direct analogue in standard document IE.

• Compared to Question Answering (QA): Recent approaches to DST explicitly frame slot value prediction as a reading comprehension QA task — “what is the value of hotel.area given the dialogue context?” — and apply QA models (BERT-for-QA, SQuAD-trained models) directly to DST. The key difference is that DST must answer this question for potentially 35+ (domain, slot) pairs simultaneously, over a context that spans many turns, and must handle cases where the “answer” is not a span in the text (e.g., “dontcare”, “none”, carry-over values).

• Compared to Coreference Resolution: Coreference resolution identifies which mentions in a document refer to the same entity (e.g., “he” → “John Smith”). DST must implicitly perform coreference resolution to correctly interpret slot value references — “near there” requires resolving “there” to the location established in a prior turn. DST models that do not explicitly model coreference rely on their encoder’s attention mechanism to learn these associations implicitly, which works for common patterns but fails on complex cross-domain references.

• Compared to Slot Filling in Information Extraction: Slot filling in the IE sense populates a template with information extracted from a document corpus. DST’s slot filling is a special case that operates over a conversational context window where the “document” is dynamically growing and subject to user modification and correction — making it inherently more complex than static document slot filling.

• Compared to Sentiment Analysis: Sentiment analysis infers affective polarity (positive/negative/neutral) from text. While not directly related to DST, sentiment and emotion signals can serve as auxiliary inputs to DST systems — for example, detected user frustration may signal that a prior belief state update was incorrect, triggering a clarification dialogue act from the Dialogue Policy.

• Compared to Intent Classification: Intent classification assigns a single categorical intent label (e.g., BookHotel, FindRestaurant) to each user utterance. DST subsumes intent classification as a special case — the domain classifier in a DST system performs multi-label intent detection across dialogue domains — but additionally tracks all slot values associated with each detected intent across the dialogue history, providing a much richer structured representation of user goals.

  MultiWOZ Slot Taxonomy Quick Reference

  The MultiWOZ 2.4 ontology defines the following domains, slot types, and representative values:

  Hotel Domain (13 slots):

• hotel.name — name of the hotel (open-vocabulary)

• hotel.area — north | south | east | west | centre

• hotel.pricerange — cheap | moderate | expensive

• hotel.type — hotel | guesthouse

• hotel.parking — yes | no

• hotel.internet — yes | no

• hotel.stars — 0 | 1 | 2 | 3 | 4 | 5

• hotel.day — monday | tuesday | … | sunday

• hotel.stay — 1 | 2 | 3 | 4 | 5 (number of nights)

• hotel.people — 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8

• hotel.book_day — day of the week for the reservation

• hotel.book_stay — duration of stay

• hotel.book_people — number of guests

  Restaurant Domain (7 slots):

• restaurant.name — open-vocabulary

• restaurant.area — north | south | east | west | centre

• restaurant.pricerange — cheap | moderate | expensive

• restaurant.food — open-vocabulary (cuisine type)

• restaurant.day, restaurant.time, restaurant.people — open-vocabulary

  Train Domain (6 slots):

• train.departure — open-vocabulary (station name)

• train.destination — open-vocabulary

• train.day — day of week

• train.leaveat — time (open-vocabulary)

• train.arriveby — time (open-vocabulary)

• train.people — integer

  Taxi Domain (4 slots):

• taxi.departure, taxi.destination — open-vocabulary

• taxi.leaveat, taxi.arriveby — time (open-vocabulary)

  Attraction Domain (3 slots):

• attraction.name — open-vocabulary

• attraction.area — directional

• attraction.type — cinema | college | entertainment | museum | nightclub | park | restaurant | sport | theatre | multiple sports

  The distinction between categorical slots (hotel.area, hotel.pricerange, hotel.stars) and open-vocabulary slots (hotel.name, restaurant.food, train.departure) is a primary design choice for DST architecture, as the two categories require fundamentally different prediction strategies.

  DST Architecture Comparison Summary

  Approach	Value Prediction	Domain Transfer	Speed	Interpretability
  Ontology Classifier (NBT, GLAD)	Classification over fixed value list	Poor (per-domain ontology required)	Fast	High
  Span Extraction (DS-DST, BERT-DST)	Token span from dialogue history	Moderate (open-vocabulary)	Fast	Medium-High
  Generative (TRADE, SimpleTOD)	Autoregressive decode	Good (copy mechanism)	Medium	Medium
  Hybrid (TripPy, SOM-DST)	Classification + span + carryover	Good	Medium	Medium
  In-Context LLM (IC-DST)	In-context generation	Very Good (few-shot)	Slow (API)	Low-Medium
  Zero-Shot Function Call (FnCTOD)	Structured function call output	Excellent (zero-shot)	Slow (API)	High (schema explicit)
  Graph-Enhanced (NoEticGraph)	Classification + attention	Good (schema transfer)	Medium	Medium
  LLM Summarisation (2025)	Two-stage: summarise then extract	Good	Slow (2 passes)	Very High

  Key architectural trade-offs across DST approaches:

• Accuracy vs. Speed: Ontology classifiers are fastest (10-30ms) but limited to predefined value sets; LLM-based zero-shot approaches are most accurate for novel domains but incur 300-1500ms API latency.

• Domain Portability vs. Annotation Cost: Fine-tuned specialist models require 500-5000 annotated dialogues per domain; zero-shot LLM approaches require only schema definitions but show lower accuracy on complex domains.

• Interpretability vs. Flexibility: Span extraction provides interpretable evidence (highlighted text span) but cannot handle implicit values; LLM generation is flexible but less interpretable.

• Robustness vs. Efficiency: Hybrid approaches (TripPy, SOM-DST) achieve strong accuracy on MultiWOZ but are complex to deploy; simpler end-to-end LLM approaches are easier to deploy but harder to audit.

  Toolkit and Framework Ecosystem

  The DST research and deployment ecosystem is supported by a set of open-source toolkits, pre-trained models, and evaluation frameworks:

• ConvLab / ConvLab-2 / ConvLab-3 (Zhu et al., 2020): The most comprehensive open-source toolkit for building, evaluating, and diagnosing end-to-end task-oriented dialogue systems. ConvLab provides modular implementations of NBT, TRADE, TripPy, and other DST models alongside NLU, NLG, and dialogue policy modules, enabling systematic ablation and module-swapping experiments. ConvLab-2 supports evaluation on MultiWOZ 2.1 and SGD. Available at github.com/ConvLab/ConvLab-2.

• Rasa Open Source (Bocklisch et al., 2017): A production-grade open-source framework for task-oriented dialogue systems with a built-in NLU pipeline (intent classification, entity extraction equivalent to slot filling), story-based dialogue management, and REST/webhook API serving. Rasa’s slot-filling mechanism implements a simplified form of DST suitable for single-domain or limited multi-domain enterprise chatbots. Widely deployed in enterprise customer service applications, including NHS-adjacent deployments.

• Hugging Face Transformers: Provides pre-trained model weights and fine-tuning pipelines for BERT-based DST architectures (BERT, RoBERTa, DeBERTa, GPT-2). The model hub hosts several fine-tuned DST models for MultiWOZ (e.g., simpletod-multiwoz, trade-dst). transformers 4.x enables straightforward fine-tuning of any encoder-decoder model for TRADE-style generative DST.

• MinTL Toolkit (Lin et al., 2020): Lightweight transfer learning framework for TOD systems, providing pre-trained models (Leaky Dialogue adapters on T5/BART) and fine-tuning scripts for DST, NLG, and end-to-end dialogue on MultiWOZ 2.0/2.1.

• SimpleTOD Repository (Salesforce Research): Official code and model checkpoints for the SimpleTOD GPT-2-based end-to-end TOD system on MultiWOZ 2.1, providing a baseline for end-to-end systems and a starting point for LLM-based DST experimentation.

• ParlAI (Miller et al., Facebook AI Research): A framework for sharing, training, and evaluating dialogue models across many tasks including DST, open-domain dialogue, QA, and retrieval. Supports MultiWOZ, ATIS, and multiple other dialogue benchmarks. Model zoo provides pre-trained open-domain dialogue models (BlenderBot family).

• DialoGLUE Benchmarking Suite (Mehri et al., 2020): Standardised NLU evaluation across seven task-oriented dialogue benchmarks including slot filling (ATIS, SNIPS, TOP), intent detection, and dialogue state tracking, enabling systematic comparison of pre-trained encoder performance across dialogue-relevant sub-tasks.

• OpenAI Function Calling API: As of 2023-2026, OpenAI’s function calling API provides the primary mechanism for LLM-based zero-shot DST in production — the developer defines dialogue domains as JSON function schemas, and GPT-4/GPT-4o/GPT-4.5 emits structured function call outputs encoding the belief state update at each turn. This approach (formalised in the FnCTOD paper) has become the de facto LLM-based DST pattern for enterprise deployments.

• LangChain / LangGraph: Framework for constructing LLM-based application pipelines and multi-agent workflows. LangGraph’s stateful agent architecture naturally implements a DST-like state accumulation pattern — each node in the agent graph can update a shared state object encoding the current belief state — making it usable as an implicit DST mechanism for agentic dialogue systems.

• Botpress: Open-source enterprise dialogue platform supporting multi-turn conversation management with explicit slot tracking, entity extraction, and intent classification — implementing a simplified DST pipeline without explicit belief state probabilistic representation. Used in commercial deployments across European enterprises.

  Key Industry Systems Using DST

  The following commercial systems employ dialogue state tracking as a core architectural component:

• Amazon Alexa: Slot-based DST across thousands of skill domains; NLU module identifies active intent and fills required slots; multi-turn conversation management handles slot clarification and confirmation.

• Google Assistant: Hybrid NLU + DST system powering search, smart home, and productivity task dialogues; schema-driven architecture derived from the SGD research programme.

• Rasa-Based Enterprise Systems: Thousands of enterprise chatbots (insurance, banking, telecoms, healthcare) built on the Rasa NLU framework use Rasa’s slot-filling as a simplified DST mechanism.

• EBO AI (NHS App Integration): UK-based platform integrated directly into the NHS App providing multilingual patient dialogue with explicit slot tracking for appointment types, NHS number, date preferences, and accessibility requirements.

• Salesforce Einstein Bots: Enterprise customer service bots with slot-based DST for case type, account identification, issue description, and resolution path determination.

• Microsoft Bot Framework / Power Virtual Agents: Enterprise dialogue platform with slot-filling flows used extensively in UK public sector and FTSE 100 company deployments.

• IBM watsonx Assistant: Enterprise dialogue system with entity extraction and slot filling across banking, insurance, telecoms, and retail sectors.

  State-of-the-Art Performance Summary (MultiWOZ, as of 2025)

  Key reported Joint Goal Accuracy (JGA) scores on MultiWOZ benchmark versions, indicating the progression of DST performance:

  MultiWOZ 2.0 (original annotations, superseded):

• GLAD (2018): 35.6% JGA

• TRADE (2019): 48.6% JGA

• TripPy (2020): 55.3% JGA

  MultiWOZ 2.1 (corrected annotations, widely used):

• TRADE baseline: ~45.6% JGA

• SOM-DST: 51.7% JGA

• TripPy: 55.0% JGA

• SimpleTOD (end-to-end, incl. NLG): 55.7% JGA

• Multi-Level Neural Belief Tracker (Kim et al., ICLR 2025): 50.91% JGA (end-to-end multi-domain)

• FnCTOD (GPT-4, zero-shot function calling): 57.6% JGA

  MultiWOZ 2.2 (further annotation corrections):

• PPTOD: 53.4% JGA

• FnCTOD (GPT-3.5-turbo, zero-shot): 51.4% JGA

• FnCTOD (GPT-4, zero-shot): 57.1% JGA

• FnCTOD (Mistral-7B, fine-tuned with function schemas): 57.8% JGA

  MultiWOZ 2.4 (most corrected annotations — recommended for new work):

• Leading systems: ~60-65% JGA as of 2025 (specific results vary by model variant)

  Note: JGA differences across versions reflect annotation corrections rather than genuine performance changes; systems evaluated on 2.0 annotations cannot be directly compared to systems evaluated on 2.4.

  Research Timeline: DST Milestones

• 1966 — ELIZA (Weizenbaum, MIT): First conversational system, uses pattern matching without any explicit state tracking; conversation context entirely discarded between turns.

• 1990s — ATIS benchmark: First formal slot-filling evaluation framework for spoken language understanding, establishing the (intent, slots) representation that directly prefigures belief state tracking.

• 1999-2002 — DARPA Communicator: First large-scale spoken dialogue systems research programme; Cambridge and CMU develop early probabilistic dialogue state tracking using Hidden Markov Models and n-gram language models.

• 2008 — Young et al., Cambridge: Introduces the Partially Observable Markov Decision Process (POMDP) formulation for dialogue management, recasting dialogue state as a hidden variable to be inferred via Bayesian filtering.

• 2010 — BUDS (Bayesian Update of Dialogue State): First practical Bayesian belief state updating framework; establishes the vocabulary of belief state, marginal belief, slot, and domain that persists in all subsequent work.

• 2013 — DSTC 1 (Williams & Young): First Dialogue State Tracking Challenge, using the DSTC bus information corpus; 9 teams participate, establishing benchmark-driven DST research as a community practice.

• 2014 — DSTC 2 (Henderson et al.): Restaurant booking domain; introduces word-level RNN approaches to DST; establishes SIGDIAL as the primary community for DST research.

• 2015 — Henderson et al.: RNN-based belief trackers using word-level features; first neural DST systems competitive with rule-based approaches on the DSTC 2 benchmark.

• 2017 — Neural Belief Tracker (Mrkšić et al., ACL): First portable neural DST using pre-trained word vectors; eliminates per-domain feature engineering; demonstrates semantic generalisation across slot values.

• 2018 — MultiWOZ 1.0 (Budzianowski et al., EMNLP): 10,438-dialogue multi-domain benchmark across 7 domains; becomes the de facto standard DST evaluation corpus; JGA < 30% for initial systems.

• 2019 — TRADE (Wu et al., ACL): Transferable dialogue state generator with copy mechanism; first cross-domain transfer DST model; JGA 48.6% on MultiWOZ 2.0.

• 2019 — SOM-DST (Kim et al.): Selective overwriting memory with explicit CARRYOVER/UPDATE/DONTCARE state operations; improves inference efficiency.

• 2020 — TripPy (Heck et al., SIGDIAL): Triple copy strategy; explicitly handles carry-over, span extraction, and classification as three separate prediction strategies per slot.

• 2020 — SimpleTOD (Hosseini-Asl et al., NeurIPS): GPT-2 fine-tuned end-to-end on MultiWOZ; first demonstration that a single language model can implicitly learn all TOD pipeline sub-tasks including DST.

• 2020 — MultiWOZ 2.1 (Eric et al.): 32.6% annotation error correction; substantial JGA score revisions across all published models.

• 2020 — Schema-Guided Dialogue (Rastogi et al., AAAI): 16,142 dialogues across 20 domains with explicit schema generalisation evaluation; first large-scale zero-shot domain transfer benchmark.

• 2021 — TOD-BERT (Wu et al.): Task-oriented dialogue pre-training on heterogeneous dialogue corpora; substantially improves few-shot DST fine-tuning efficiency.

• 2022 — IC-DST (Hu et al., EMNLP): In-context learning for DST with ChatGPT; first demonstration of competitive LLM-based zero-shot DST.

• 2022 — MultiWOZ 2.4 (Ye et al.): Most comprehensive annotation correction; recommended version for new DST system evaluation.

• 2023 — OrchestraLLM (Guo & Klakow): Multi-LLM routing for efficient DST inference; different-scale LLMs handle different DST sub-tasks.

• 2024 — FnCTOD (Feng et al., ACL): Zero-shot DST via function-schema prompting; 7B open-source models surpass ChatGPT on MultiWOZ 2.2; JGA 57.1%.

• 2025 — Noetic Graph DST (Wang & Zhang, Pattern Recognition): Graph attention networks over dialogue entity graphs; 1.7% SGD improvement over all domains.

• 2025 — LLM Summarisation DST (Li et al., arXiv): Natural language dialogue summaries as intermediate representations; improved interpretability and cross-domain transfer.

• 2025 — Multi-Level Neural Belief Tracker (Kim et al., ICLR): Slot-level and domain-level belief tracking signals combined via late fusion; state-of-the-art end-to-end multi-domain performance.

  Key Terminology

• Belief State: The accumulated representation of confirmed conversational context at a given dialogue turn, encoding the current system estimate of what the user wants. Formally expressed as a set of (domain, slot, value) triples; in probabilistic systems, as a probability distribution over possible state configurations. The belief state is updated at each turn by integrating the new user utterance with the prior belief state.

• Slot: A named parameter in a domain ontology representing a specific aspect of the user’s goal (e.g., hotel.area, restaurant.pricerange, train.leaveAt). Slots are defined per domain and may be typed (categorical, time, date, boolean, free-text).

• Slot Value: The current value of a slot as confirmed from the dialogue context (e.g., hotel.area = “centre”, restaurant.pricerange = “moderate”). May be a fixed ontology value (for categorical slots), a string extracted from the dialogue (for open-vocabulary slots), or the special value “dontcare” indicating the user has no constraint on that slot.

• Domain: A top-level category grouping related slots into a coherent service area (hotel, restaurant, train, taxi, attraction, etc.). A multi-domain dialogue may activate multiple domains simultaneously or sequentially.

• Joint Goal Accuracy (JGA): The primary DST evaluation metric. A binary score per turn indicating whether all predicted slot values across all active domains exactly match the gold-standard annotation.

• Carry-Over: The propagation of unchanged slot-value pairs from the belief state at turn t-1 to turn t. Efficient carry-over reduces prediction computation; correct carry-over requires distinguishing unchanged values from slots that the user has implicitly modified or cancelled.

• Ontology: The formal specification of domains, slots, and candidate values for a given dialogue system. In ontology-constrained DST, the model is restricted to predicting values within the enumerated ontology; in open-vocabulary DST, values are predicted from the dialogue history text or generated by the language model.

• Open-Vocabulary DST: A DST setting where slot values are not restricted to a predefined candidate list, allowing the model to predict any string as a slot value. Required for slots like hotel.name, restaurant.name, taxi.destination where the possible values are unbounded.

• Schema-Guided DST: A DST setting where the domain schema (slot names, types, descriptions) is provided as natural language input to the model alongside the dialogue history, enabling generalisation to unseen schemas without domain-specific training data (as in Google’s SGD benchmark).

• Dialogue State Tracking Challenge (DSTC): An annual shared task series inaugurated in 2013 that provides standardised corpora, evaluation metrics, and competitive benchmarking for DST systems. DSTC has catalysed over a decade of DST research progress and produced the primary benchmark datasets (DSTC 1-3 corpora, later adopted and extended into MultiWOZ and SGD).

• Wizard-of-Oz (WOZ): A data collection methodology where a hidden human operator (“wizard”) responds to users who believe they are interacting with an automated system. WOZ dialogues provide naturalistic user behaviour because participants do not know they are talking to a human, producing more varied and challenging language than dialogues collected from users who know the system’s capabilities. MultiWOZ and its predecessors were collected using WOZ methodology.

• Function Calling DST: A zero-shot DST approach where the LLM is provided with domain definitions encoded as typed function schemas in the system prompt, and produces belief state updates as structured function call outputs — enabling machine-readable, executable belief state representations without domain-specific fine-tuning.

  Research and Literature

  1. Young, S., Gašić, M., Keizer, S., Mairesse, F., Schatzmann, J., Thomson, B., & Yu, K. (2010). The hidden information state model: A practical framework for POMDP-based spoken dialogue management. Computer Speech and Language, 24(2), 150-174.
  2. Young, S., Gašić, M., Thomson, B., & Williams, J. D. (2013). POMDP-based statistical spoken dialogue systems: A review. Proceedings of the IEEE, 101(5), 1160-1179.
  3. Henderson, M., Thomson, B., & Williams, J. (2014). The second dialog state tracking challenge. SIGDIAL 2014.
  4. Henderson, M., Thomson, B., & Young, S. (2014). Word-based dialog state tracking with recurrent neural networks. SIGDIAL 2014.
  5. Williams, J. D., Raux, A., & Henderson, M. (2016). The dialog state tracking challenge series: A review. Dialogue and Discourse, 7(3), 4-33.
  6. Mrkšić, N., Séaghdha, D. Ó., Wen, T.-H., Thomson, B., & Young, S. (2017). Neural belief tracker: Data-driven dialogue state tracking. ACL 2017.
  7. Budzianowski, P., Wen, T.-H., Tseng, B.-H., Casanueva, I., Ultes, S., Ramadan, O., & Gašić, M. (2018). MultiWOZ — a large-scale multi-domain Wizard-of-Oz dataset for task-oriented dialogue modelling. EMNLP 2018.
  8. Wu, C.-S., Madotto, A., Hosseini-Asl, E., Xiong, C., Socher, R., & Fung, P. (2019). Transferable multi-domain state generator for task-oriented dialogue systems (TRADE). ACL 2019.
  9. Kim, S., Yang, S., Kim, G., & Lee, S. W. (2019). Efficient dialogue state tracking by selectively overwriting memory (SOM-DST). ACL 2020.
  10. Heck, M., van Niekerk, C., Lubis, N., Geishauser, C., Lin, H.-C., Moresi, M., & Gašić, M. (2020). TripPy: A triple copy strategy for value independent neural dialog state tracking. SIGDIAL 2020.
  11. Hosseini-Asl, E., McCann, B., Wu, C.-S., Yavuz, S., & Socher, R. (2020). A simple language model for task-oriented dialogue (SimpleTOD). NeurIPS 2020.
  12. Lin, Z., Xu, B., Liu, G., Winata, G. I., Xu, P., Shin, J., … & Fung, P. (2020). MinTL: Minimalist transfer learning for task-oriented dialogue systems. EMNLP 2020.
  13. Peng, B., Li, C., Li, J., Shayandeh, S., Liden, L., & Gao, J. (2021). SOLOIST: Building task bots at scale with transfer learning and machine teaching. TACL 2021.
  14. Hu, Y., Lee, Y.-S., Xu, T., Moon, S., Shu, C., & Liu, H. (2022). In-context learning for few-shot dialogue state tracking (IC-DST). EMNLP 2022.
  15. Lee, Y.-S., Xu, T., Moon, S., Madotto, A., & Liu, H. (2022). SGD-X: A benchmark for robust generalisation in schema-guided dialogue state tracking. AAAI 2022.
  16. Ma, Y., Cao, J., Hong, Y., & Sun, X. (2022). Rethinking dialogue state tracking with reasoning. IJCAI 2022.
  17. Guo, D., & Klakow, D. (2023). OrchestraLLM: Efficient orchestration of language models for dialogue state tracking. arXiv:2311.09758.
  18. Feng, W., Qian, C., Yu, T., Chen, B., McKeown, K., Wang, H., & Mi, H. (2024). Large language models as zero-shot dialogue state trackers through function calling. ACL 2024.
  19. Heck, M., Geishauser, C., Lin, H.-C., Lubis, N., Moresi, M., van Niekerk, C., & Gašić, M. (2024). DSTC 11 track 5: Task-oriented conversational modelling with subjective knowledge. DSTC 11.
  20. He, R., Xu, J., & Zhang, Z. (2024). Intent-driven in-context learning for few-shot dialogue state tracking. arXiv:2412.03270.
  21. Wang, J., & Zhang, L. (2025). Advanced dialog state tracking with noetic graphs for complex human-machine interactions. Pattern Recognition, Elsevier. doi:10.1016/j.patcog.2025.111842.
  22. Li, Y., Chen, X., & Zhao, J. (2025). Interpretable and robust dialogue state tracking via natural language summarisation with LLMs. arXiv:2503.08857.
  23. Ye, F., Manotumruksa, J., & Yilmaz, E. (2022). MultiWOZ 2.4: A multi-domain task-oriented dialogue dataset with essential annotation corrections to improve state tracking evaluation. SIGDIAL 2022.
  24. Rastogi, A., Zang, X., Sunkara, S., Gupta, R., & Khaitan, P. (2020). Towards scalable multi-domain conversational agents: The schema-guided dialogue dataset. AAAI 2020.
  25. Eric, M., Goel, R., Paul, S., Sethi, A., Agarwal, S., Gao, S., … & Hakkani-Tür, D. (2020). MultiWOZ 2.1: Multi-domain dialogue state corrections and state tracking baselines. LREC 2020.
  26. Zeng, Y., Nie, J.-Y., & Su, H. (2022). Dialogue state tracking based on hierarchical slot attention and contrastive learning. CIKM 2022.
  27. Vulic, I., Budzianowski, P., Henderson, M., & Gasic, M. (2021). Dialogue state tracking with a language model using schema-driven prompting. EMNLP 2021.
  28. Kim, B., Kim, J., Kim, M., & Lee, S.-G. (2025). End-to-end multi-domain task-oriented dialogue systems with multi-level neural belief tracker. OpenReview / ICLR 2025.

Provenance