Disfluency Detection using a Bidirectional LSTM
Table 7: F scores of different types of edits for the CRF and BLSTM on the dev set.
['[BOLD] Model ( [ITALIC] input)', '[BOLD] Repetitions', '[BOLD] Other', '[BOLD] Either']
Repetition disfluencies are much easier to detect than other disfluencies, although not trivial since some repetitions can be fluent. In order to better understand model performance, we evaluate the 17-state models in terms of their ability to detect repetition vs. non-repetition (other) reparanda. We conjecture that the dense word representation in the BLSTM captures more of the reparandum/repair “rough copy” similarities than the simple POS pattern match features.