Multi-Task Self-Supervised Learning for Disfluency Detection
Table 2: Experiment results on English Switchboard data, where “Full” means the results using 100% human-annotated data, and “1000 sents” means the results using less than 1% (1000 sentences) human-annotated data.
['Method', 'Full Dev', 'Full Dev', 'Full Dev', 'Full Test', 'Full Test', 'Full Test', '1000 sents Dev', '1000 sents Dev', '1000 sents Dev', '1000 sents Test', '1000 sents Test', '1000 sents Test']
We can see that our self-supervised method outperforms the baseline methods in all the settings. Surprisingly, our self-supervised method achieves almost 20 point improvements over transition-based method when using less than 1% (1000 sentences) human-annotated disfluency detection data.