Disfl-QA
Introduced by Gupta et al. in Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.
The final dataset consists of ~12k (disfluent question, answer) pairs. Over 90% of the disfluencies are corrections or restarts, making it a much harder test set for disfluency correction. Disfl-QA aims to fill a major gap between speech and NLP research community. We hope the dataset can serve as a benchmark dataset for testing robustness of models against disfluent inputs.
Papers
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