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.
Robust cross-domain disfluency detection with pattern match networks
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abstract
In this paper we introduce a novel pattern match neural network architecture that uses neighbor similarity scores as features, eliminating the need for feature engineering in a disfluency detection task. We evaluate the approach in disfluency detection for four different speech genres, showing that the approach is as effective as hand-engineered pattern match features when used on in-domain data and achieves superior performance in cross-domain scenarios.
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cs.CL 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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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.