@InProceedings{wang-EtAl:2017:EMNLP20178,
  author    = {Wang, Shaolei  and  Che, Wanxiang  and  Zhang, Yue  and  Zhang, Meishan  and  Liu, Ting},
  title     = {Transition-Based Disfluency Detection using LSTMs},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1296}
}