Artificial word insertions and deletions in news text, used as self-supervised pretraining tasks, transfer to human-annotated disfluency detection and cut the labeled-data requirement to 1,000 sentences.
Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution
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abstract
Most existing approaches for zero pronoun resolution are heavily relying on annotated data, which is often released by shared task organizers. Therefore, the lack of annotated data becomes a major obstacle in the progress of zero pronoun resolution task. Also, it is expensive to spend manpower on labeling the data for better performance. To alleviate the problem above, in this paper, we propose a simple but novel approach to automatically generate large-scale pseudo training data for zero pronoun resolution. Furthermore, we successfully transfer the cloze-style reading comprehension neural network model into zero pronoun resolution task and propose a two-step training mechanism to overcome the gap between the pseudo training data and the real one. Experimental results show that the proposed approach significantly outperforms the state-of-the-art systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.
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cs.CL 1years
2019 1verdicts
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Multi-Task Self-Supervised Learning for Disfluency Detection
Artificial word insertions and deletions in news text, used as self-supervised pretraining tasks, transfer to human-annotated disfluency detection and cut the labeled-data requirement to 1,000 sentences.