QuAC : Question Answering in Context
This dataset addresses the problem of context-aware question answering for researchers, though it is incremental as it builds on existing QA datasets by adding dialog-based challenges.
The authors introduced QuAC, a dataset of 14K information-seeking QA dialogs with 100K questions, designed to challenge models with open-ended and context-dependent queries. Their best model underperformed humans by 20 F1, indicating substantial room for improvement.
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.