A sequence-to-sequence model is a neural architecture that maps an input sequence of arbitrary length to an output sequence of arbitrary length, classically using an encoder to compress the input into a context representation and a decoder to generate the output one element at a time. Originally built from recurrent networks such as LSTMs and GRUs and later augmented with attention to overcome the fixed-context bottleneck, the paradigm became the foundation for the transformer. Sequence-to-sequence models power machine translation, text summarisation, speech recognition, and other tasks where input and output structures differ.
Overview
- Sequence To Sequence Model sits within the broader category of Neural Network, which it specialises.
- It connects to a network of 14 related classes across the knowledge graph, anchoring edges that previously pointed to an undefined node.
Key aspects
- uses: LSTM
- uses: GRU
- uses: Attention Mechanism
- uses: Beam Search
- implements: Encoder-Decoder
- implements: Recurrent Neural Network
Mechanisms
- Sequence To Sequence Model operates through its constituent parts and dependencies, integrating with adjacent systems to deliver its function within artificial intelligence.
Applications
- Applied to advance Machine Translation.
- Applied to advance Text Summarisation.
- Applied to advance Speech Recognition.
- Applied to advance Natural Language Processing.