QuAC: Question Answering in Context

@inproceedings{Choi2018QuACQA,
  title={QuAC: Question Answering in Context},
  author={Eunsol Choi and He He and Mohit Iyyer and Mark Yatskar and Wen-tau Yih and Yejin Choi and Percy S. Liang and Luke S. Zettlemoyer},
  booktitle={EMNLP},
  year={2018}
}
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… CONTINUE READING
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