Boosting Disfluency Detection with Large Language Model as Disfluency Generator
This addresses the data scarcity issue in disfluency detection for natural language processing applications, but it is incremental as it builds on existing augmentation methods.
The paper tackles the problem of scarce human-annotated data for disfluency detection by proposing a lightweight data augmentation approach using a large language model (LLM) to generate realistic disfluent sentences, achieving state-of-the-art results with improved cost-effectiveness.
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. Our code is available here.