Multi-Task Self-Supervised Learning for Disfluency Detection
Table 3: Comparison with previous state-of-the-art methods on the test set of English Switchboard. “Full” means using 100% human-annotated data for fine-tuning, and “1000 sents” means using less than 1% (1000 sentences) human-annotated data for fine-tuning.
['[BOLD] Method', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
We compare our self-supervised model to five top performing systems, which rely on large-scale human-annotated data and complicated hand-crafted features. Our model outperforms the state-of-the-art, achieving a 90.2% F1-score We attribute the success to the strong ability to learn global sentence-level structural information. Surprisingly, with less than 1% (1000 sentences) human-annotated training data, our model achieves comparable F1-score as the previous top performing systems using 100% human-annotated training data, which shows that our self-supervised method can substantially reduce the need for human-annotated training data. Note that we do not compare our work with the work of \citeauthorC18-1299 ̵̃\shortciteC18-1299 using semi-supervised method for disfluency detection.