Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing
This work addresses efficiency bottlenecks in ASR post-editing for speech processing applications, representing an incremental improvement over prior edit span methods.
The paper tackles the computational inefficiency of LLM-based rewriting by proposing phrasal edit representations, achieving a 50-60% reduction in the WER gap compared to full rewrites while maintaining 80-90% of the length reduction efficiency.
Large Language Models (LLMs) excel at rewriting tasks such as text style transfer and grammatical error correction. While there is considerable overlap between the inputs and outputs in these tasks, the decoding cost still increases with output length, regardless of the amount of overlap. By leveraging the overlap between the input and the output, Kaneko and Okazaki (2023) proposed model-agnostic edit span representations to compress the rewrites to save computation. They reported an output length reduction rate of nearly 80% with minimal accuracy impact in four rewriting tasks. In this paper, we propose alternative edit phrase representations inspired by phrase-based statistical machine translation. We systematically compare our phrasal representations with their span representations. We apply the LLM rewriting model to the task of Automatic Speech Recognition (ASR) post editing and show that our target-phrase-only edit representation has the best efficiency-accuracy trade-off. On the LibriSpeech test set, our method closes 50-60% of the WER gap between the edit span model and the full rewrite model while losing only 10-20% of the length reduction rate of the edit span model.