@inproceedings{wang-etal-2017-transition,
title = "Transition-Based Disfluency Detection using {LSTM}s",
author = "Wang, Shaolei and
Che, Wanxiang and
Zhang, Yue and
Zhang, Meishan and
Liu, Ting",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1296",
doi = "10.18653/v1/D17-1296",
pages = "2785--2794",
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.",
}
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%0 Conference Proceedings
%T Transition-Based Disfluency Detection using LSTMs
%A Wang, Shaolei
%A Che, Wanxiang
%A Zhang, Yue
%A Zhang, Meishan
%A Liu, Ting
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-transition
%X 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.
%R 10.18653/v1/D17-1296
%U https://aclanthology.org/D17-1296
%U https://doi.org/10.18653/v1/D17-1296
%P 2785-2794
Markdown (Informal)
[Transition-Based Disfluency Detection using LSTMs](https://aclanthology.org/D17-1296) (Wang et al., EMNLP 2017)
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.