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Boosting Disfluency Detection with Large Language Model as Disfluency Generator
March 14, 2024, 4:48 a.m. | Zhenrong Cheng, Jiayan Guo, Hao Sun, Yan Zhang
cs.CL updates on arXiv.org arxiv.org
Abstract: 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 …
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