Multi-Task Self-Supervised Learning for Disfluency Detection
Table 6: F1-score of different types of reparandums on English Switchboard test data.
['[BOLD] Method', '[BOLD] Repet', '[BOLD] Non-repet', '[BOLD] Either']
Repetition disfluencies are easier to detect and even some simple hand-crafted features can handle them well. In order to better understand model performances, we evaluate our model’s ability to detect repetition vs. non-repetition (other) reparandum. All three models achieve high scores on repetition reparandum. Our self-supervised model is much better in predicting non-repetitions compared to the two baseline methods. We conjecture that our self-supervised tasks can capture more sentence-level structural information.