Cold Fusion Seq2Seq Models for Domain Adaptation
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Solution Overview
Problem
Sequence-to-sequence (Seq2Seq) models face limitations when integrating language models, including redundant information learning and bias towards training labels, which hinders their ability to adapt to new domains effectively.
Innovation Solution
The Cold Fusion approach trains a Seq2Seq model together with a pre-trained language model, using both hidden states for gate computation and a fine-grained gating mechanism to leverage language information, allowing the model to learn task-relevant information and adapt to new domains with less data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a Seq2Seq model is trained to output complete label sequences without a language model, then the decoder learns an implicit language model from training labels, but this takes up a significant portion of decoder capacity to learn redundant information
Solution Approach 1:
The patent extracts the language modeling function from the Seq2Seq decoder by introducing a separate neural language model. The language model is trained independently on large corpora and then integrated with the Seq2Seq model through fusion mechanisms (shallow fusion at output level or deep fusion at hidden state level), allowing the decoder to focus on sequence transformation while the dedicated language model handles linguistic patterns.
Solution Approach 2:
The patent creates a multi-functional system where the neural language model serves multiple purposes: it provides language priors during decoding, improves generalization to unseen sequences, and can be applied across different Seq2Seq tasks (machine translation, speech recognition, etc.). The fused model combines the task-specific Seq2Seq capabilities with the general-purpose language modeling capabilities.
2Reliability
If a Seq2Seq model is fully trained on legal documents and then fused with a medical language model, then the decoder has an inherent tendency to follow the linguistic structure found in legal texts, but this bias hinders adaptation to novel domains
Solution Approach 1:
The patent implements dynamic domain adaptation by making the fusion between Seq2Seq and language model adaptive rather than static. The model can dynamically adjust the influence of the language model based on the input domain, allowing it to leverage domain-specific linguistic patterns when available while maintaining flexibility to adapt to new domains. The system can be fine-tuned on domain-specific data to adjust the fusion weights and achieve optimal performance for each domain.
3Reliability
If Deep Fusion learns to fuse hidden states of Seq2Seq decoder and neural language model after independent training, then performance improves over baseline, but the model must first learn to discount the implicit knowledge of the language
Solution Approach 1:
The patent applies preliminary action by pre-training the neural language model on large corpora before integrating it with the Seq2Seq model. This pre-trained language model already contains learned linguistic knowledge and can be immediately utilized during Seq2Seq training and decoding. The pre-training eliminates the need for the fused model to learn language patterns from scratch, significantly reducing training time while maintaining performance benefits.
Data Source
AI summary
Described herein are systems and methods for generating natural language sentences with Sequence-to-sequence (Seq2Seq) models with attention. The Seq2Seq models may be implemented in applications, such as machine translation, image captioning, and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language models. Disclosed herein are “Cold Fusion” architecture embodiments that leverage a pre-trained language model during training. The Seq2Seq models with Cold Fusion embodiments are able to better utilize language information enjoying faster convergence, better generalization, and almost complete transfer to a new domain while using less labeled training data.