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Transition-Based Disfluency Detection using LSTMs

2017-09-01EMNLP 2017Code Available0· sign in to hype

Shaolei Wang, Wanxiang Che, Yue Zhang, Meishan Zhang, Ting Liu

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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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