Boosting Disfluency Detection with Large Language Model as Disfluency
  Generator
Zhenrong Cheng Jiayan Guo Hao Sun Yan Zhang
Current disfluency detection methods heavily rely on costly and scarce
human-annotated data. To tackle this issue, some approaches employ heuristic or
statistical features to generate disfluent sentences, partially improving
detection performance. However, these sentences often deviate from real-life
scenarios, constraining overall model enhancement. In this study, we propose a
lightweight data augmentation approach for disfluency detection, utilizing the
superior generative and semantic understanding capabilities of large language
model (LLM) to generate disfluent sentences as augmentation data. We leverage
LLM to generate diverse and more realistic sentences guided by specific
prompts, without the need for fine-tuning the LLM. Subsequently, we apply an
uncertainty-aware data filtering approach to improve the quality of the
generated sentences, utilized in training a small detection model for improved
performance. Experiments using enhanced data yielded state-of-the-art results.
The results showed that using a small amount of LLM-generated enhanced data can
significantly improve performance, thereby further enhancing
cost-effectiveness.