Abstract
In this paper, we study the problem of disfluency detection using the encoder-decoder framework. We treat disfluency detection as a sequence-to-sequence problem and propose a neural attention-based model which can efficiently model the long-range dependencies between words and make the resulting sentence more likely to be grammatically correct. Our model firstly encode the source sentence with a bidirectional Long Short-Term Memory (BI-LSTM) and then use the neural attention as a pointer to select an ordered sub sequence of the input as the output. Experiments show that our model achieves the state-of-the-art f-score of 86.7% on the commonly used English Switchboard test set. We also evaluate the performance of our model on the in-house annotated Chinese data and achieve a significantly higher f-score compared to the baseline of CRF-based approach.- Anthology ID:
- C16-1027
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 278–287
- Language:
- URL:
- https://aclanthology.org/C16-1027
- DOI:
- Cite (ACL):
- Shaolei Wang, Wanxiang Che, and Ting Liu. 2016. A Neural Attention Model for Disfluency Detection. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 278–287, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Neural Attention Model for Disfluency Detection (Wang et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/C16-1027.pdf