Disfluency Detection using a Bidirectional LSTM
Table 5: Comparison of the BLSTM model to state-of-the-art methods in the literature on the test set.
['[BOLD] Model', '[BOLD] P', '[BOLD] R', '[BOLD] F']
We evaluate our best models on the test set and compare them to recent methods in the literature leveraging only transcripts. For edit detection, we use the explicit repair state space (8 states), which achieves the best results on the development set, including results both with and without ILP post-processing. Both systems beat the best prior result with lexical cues only, achieving state-of-the-art performance of 85.9. Looking at the results in terms of the reduction of the performance gap from 1-F of 15.2 to 14.1, this corresponds to a 7% improvement. Incorporating prosodic features in a neural network framework is straightforward and will likely lead to an additional gain.