Disfluency Detection using a Bidirectional LSTM
Table 3: Performance of the LSTM and Bidirectional LSTM on the dev set in the edit detection tasks.
['[BOLD] Model', '[BOLD] P', '[BOLD] R', '[BOLD] F']
As shown in the table, the BLSTM has much better performance in edit detection compared to the CRF in all cases. Moreover, the BLSTM with explicit repair states achieves the best result in the edit detection task, which we hypothesize is related to the success of the noisy channel model approach: explicitly representing the extent of the repair allows the model to match the repair to the reparandum for improved detection. Although some corrections to illegal sequences do not impact edit detection performance, many do. ILP improves the precision of our BLSTM predictions without hurting the recall.