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-theart F-score on both the commonly used English Switchboard test set and a set of in-house annotated Chinese data.
The current state-of-the-art in disfluency detection uses a transition-based neural model architecture REF .
Transition-Based Disfluency Detection using LSTMs
EMNLP
Computer Science