Transition-Based Disfluency Detection using LSTMs
Shaolei Wang, Wanxiang Che, Yue Zhang, Meishan Zhang, Ting Liu
Abstract
In this paper, we model the problem of disfluency detection using a transition-based framework, which incrementally constructs and labels the disfluency chunk of input sentences using a new transition system without syntax information. Compared with sequence labeling methods, it can capture non-local chunk-level features; compared with joint parsing and disfluency detection methods, it is free for noise in syntax. Experiments show that our model achieves state-of-the-art f-score of 87.5% on the commonly used English Switchboard test set, and a set of in-house annotated Chinese data.- Anthology ID:
- D17-1296
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2785–2794
- Language:
- URL:
- https://aclanthology.org/D17-1296
- DOI:
- 10.18653/v1/D17-1296
- Cite (ACL):
- Shaolei Wang, Wanxiang Che, Yue Zhang, Meishan Zhang, and Ting Liu. 2017. Transition-Based Disfluency Detection using LSTMs. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2785–2794, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Transition-Based Disfluency Detection using LSTMs (Wang et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/D17-1296.pdf
- Code
- hitwsl/transition_disfluency