Geology

Biology

Oceanography

World Wide Web

Baseline (sea)

Philosophy

Paleontology

Artificial intelligence

Dialog box

Programming language

Natural language processing

Information retrieval

Sequence (biology)

Reading comprehension

Context (archaeology)

Computer science

Linguistics

Question answering

Comprehension

Genetics

Reading (process)

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.

QuAC: Question Answering in Context