Abstract
Question answering is an AI-complete problem, but existing datasets lack key elements of language understanding such as coreference and ellipsis resolution. We consider sequential question answering: multiple questions are asked one-by-one in a conversation between a questioner and an answerer. Answering these questions is only possible through understanding the conversation history. We introduce the task of question-in-context rewriting: given the context of a conversation’s history, rewrite a context-dependent into a self-contained question with the same answer. We construct, CANARD, a dataset of 40,527 questions based on QuAC (Choi et al., 2018) and train Seq2Seq models for incorporating context into standalone questions.- Anthology ID:
- D19-1605
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5918–5924
- Language:
- URL:
- https://aclanthology.org/D19-1605
- DOI:
- 10.18653/v1/D19-1605
- Cite (ACL):
- Ahmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. 2019. Can You Unpack That? Learning to Rewrite Questions-in-Context. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5918–5924, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Can You Unpack That? Learning to Rewrite Questions-in-Context (Elgohary et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/D19-1605.pdf
- Data
- CANARD