Disfluency Detection for Vietnamese

Mai Hoang Dao, Thinh Hung Truong, Dat Quoc Nguyen

Keywords: Disfluency Detection, Vietnamese

TL;DR: "Disfluency Detection for Vietnamese" - To appear in Proceedings of WNUT 2022

Abstract: In this paper, we present the first empirical study for Vietnamese disfluency detection. To conduct this study, we first create a disfluency detection dataset for Vietnamese, with manual annotations over two disfluency types. We then empirically perform experiments using strong baseline models, and find that: automatic Vietnamese word segmentation improves the disfluency detection performances of the baselines, and the highest performance results are obtained by fine-tuning pre-trained language models in which the monolingual model PhoBERT for Vietnamese does better than the multilingual model XLM-R.