Towards better decoding and language model integration in sequence to sequence models (1612.02695v1)

Abstract: The recently proposed Sequence-to-Sequence (seq2seq) framework advocates replacing complex data processing pipelines, such as an entire automatic speech recognition system, with a single neural network trained in an end-to-end fashion. In this contribution, we analyse an attention-based seq2seq speech recognition system that directly transcribes recordings into characters. We observe two shortcomings: overconfidence in its predictions and a tendency to produce incomplete transcriptions when LLMs are used. We propose practical solutions to both problems achieving competitive speaker independent word error rates on the Wall Street Journal dataset: without separate LLMs we reach 10.6% WER, while together with a trigram LLM, we reach 6.7% WER.

• The paper demonstrates that label smoothing and neighborhood smoothing strategies effectively mitigate model overconfidence in seq2seq ASR tasks.
• The paper introduces a coverage penalty during beam search to ensure thorough attention coverage, reducing incomplete transcriptions.
• Experimental results on the WSJ dataset show improved word error rates, achieving 10.6% without and 6.7% with a trigram language model.

Decoding and LLM Integration in Sequence-to-Sequence Models

The research paper "Towards better decoding and LLM integration in sequence to sequence models" by Jan Chorowski and Navdeep Jaitly explores the nuances of enhancing sequence-to-sequence (seq2seq) models, specifically focusing on automatic speech recognition (ASR). Utilizing an attention-based seq2seq framework, the work directly transcribes audio recordings into a sequence of characters. This model addresses inherent limitations, such as prediction overconfidence and incomplete transcriptions, especially when LLMs are integrated.

Key Contributions

The paper outlines critical observations and provides solutions to improve seq2seq architectures:

1. Seq2Seq Limitations: The seq2seq models, while operating in a discriminative training mode different from classical ASR systems, often exhibit overconfidence in predictions and produce incomplete transcriptions when complemented by external LLMs.
2. Model Overconfidence Mitigation: Overconfidence was identified as an issue due to the peaked probability distributions that result from the cross-entropy training criterion. The authors address this by implementing label smoothing strategies. Neighborhood smoothing, which propagates probability mass to temporally adjacent tokens, offers a compelling solution for improving model accuracy and aiding beam search in recovering from errors.
3. Incomplete Transcription Rectification: To counter the generation of incomplete transcripts in wide beam searches, the concept of a coverage penalty was introduced. This approach ensures thorough frame attention coverage during decoding, minimizing errors without inducing looping behaviors observed in other systems.
4. Integration with LLMs: The integration of LLMs into beam search required balancing the LLM term with a coverage term to prevent deficiencies in decoding outputs. The paper revealed that tweaking these interactions, alongside model confidence adjustment through label smoothing, markedly enhances performance.

Experimental Results

Experiments conducted on the WSJ (Wall Street Journal) dataset demonstrated that these interventions yield competitive Word Error Rates (WER). The model reached a WER of 10.6% without external LLMs and 6.7% when coupled with a trigram LLM. These results signify a marked improvement over baseline seq2seq implementations and align closely with sophisticated DNN-HMM and CTC ensemble results.

Implications and Future Directions

The implications of these findings extend to both the theoretical and application spaces of NLP and ASR:

• Practical Enhancements: The introduction of label smoothing and precise tuning of beam search components showcase methods to enhance seq2seq models' robustness in dealing with real-world noisy data and variability.
• Theoretical Insights: Insights into model overconfidence remediation and coverage during decoding contribute to a deeper understanding of the regularization and alignment dynamics in seq2seq frameworks.
• Future Research: Future avenues may explore the efficacy of global normalization methods in tandem with these regularization strategies and assess their potential to replace computationally expensive local normalization regimes. Additionally, exploration into learning-based beam search strategies might offer further efficiency and accuracy gains.

In conclusion, the paper by Chorowski and Jaitly offers substantive advancements in seq2seq model optimization, contributing valuable methodologies for enhancing ASR systems. These techniques, elucidated through rigorous experimentation, establish a strong foundation for subsequent research and development in NLP and deep learning systems.