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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2785–2794
Language:
URL:
https://www.aclweb.org/anthology/D17-1296
DOI:
10.18653/v1/D17-1296
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1296.pdf