  We introduce a new approach for disfluency detection using a Bidirectional
Long-Short Term Memory neural network (BLSTM). In addition to the word
sequence, the model takes as input pattern match features that were developed
to reduce sensitivity to vocabulary size in training, which lead to improved
performance over the word sequence alone. The BLSTM takes advantage of explicit
repair states in addition to the standard reparandum states. The final output
leverages integer linear programming to incorporate constraints of disfluency
structure. In experiments on the Switchboard corpus, the model achieves
state-of-the-art performance for both the standard disfluency detection task
and the correction detection task. Analysis shows that the model has better
detection of non-repetition disfluencies, which tend to be much harder to
detect.
