{"url":"/dataset/disfl-qa","name":"Disfl-QA","full_name":null,"description_markdown":"**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](squad) dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.\r\n\r\nThe 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.","description_withheld":null,"homepage":"https://github.com/google-research-datasets/disfl-qa","introduced_date":"2021-06-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/disfl-qa-a-benchmark-dataset-for","title":"Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering","first_author":"Aditya Gupta","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Disfl-QA","disfl_qa"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}