TY - GEN
T1 - Transition-based disfluency detection using LSTMs
AU - Wang, Shaolei
AU - Che, Wanxiang
AU - Zhang, Yue
AU - Zhang, Meishan
AU - Liu, Ting
N1 - Publisher Copyright:
© 2017 Association for Computational Linguistics.
PY - 2017
Y1 - 2017
N2 - 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 set of transition actions 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 on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.
AB - 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 set of transition actions 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 on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.
UR - https://www.scopus.com/pages/publications/85069005928
M3 - 会议稿件
AN - SCOPUS:85069005928
T3 - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 2785
EP - 2794
BT - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Y2 - 9 September 2017 through 11 September 2017
ER -