Disfluency Detection using a Bidirectional LSTM
Table 6: Comparison of 5-state CRF, LSTM and BLSTM edit detection models with different feature sets on the dev set.
['[BOLD] Model ( [ITALIC] input)', '[BOLD] P', '[BOLD] R', '[BOLD] F']
We compare the performance of all systems with words alone to the setting when all the features are used. The CRF word features include 1-3 grams within a window of 8 around the word, whereas the LSTM and BLSTM use only the current word index and incorporate longer context through the recurrent structure. For the words-only case, the LSTM and BLSTM give much better results than the CRF. When we add POS and pattern-match features, all systems improve, but the CRF benefits much more.