Incorporating External POS Tagger for Punctuation Restoration

TL;DR

This paper enhances punctuation restoration in speech recognition by integrating external POS tags and a novel sequence boundary sampling method, achieving state-of-the-art results on the IWSLT benchmark.

Contribution

It introduces a method to fuse external POS tags into language models and proposes sequence boundary sampling for improved punctuation prediction.

Findings

Consistent performance improvements over previous methods

Achieves new state-of-the-art on IWSLT benchmark

Both large language models and POS tags are crucial for performance

Abstract

Punctuation restoration is an important post-processing step in automatic speech recognition. Among other kinds of external information, part-of-speech (POS) taggers provide informative tags, suggesting each input token's syntactic role, which has been shown to be beneficial for the punctuation restoration task. In this work, we incorporate an external POS tagger and fuse its predicted labels into the existing language model to provide syntactic information. Besides, we propose sequence boundary sampling (SBS) to learn punctuation positions more efficiently as a sequence tagging task. Experimental results show that our methods can consistently obtain performance gains and achieve a new state-of-the-art on the common IWSLT benchmark. Further ablation studies illustrate that both large pre-trained language models and the external POS tagger take essential parts to improve the model's performance.