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        <title>Can You Unpack That? Learning to Rewrite Questions-in-Context</title>
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        <namePart type="given">Ahmed</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Can You Unpack That? Learning to Rewrite Questions-in-Context
%A Elgohary, Ahmed
%A Peskov, Denis
%A Boyd-Graber, Jordan
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F elgohary-etal-2019-unpack
%X 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.
%R 10.18653/v1/D19-1605
%U https://aclanthology.org/D19-1605
%U https://doi.org/10.18653/v1/D19-1605
%P 5918-5924

Markdown (Informal)

[Can You Unpack That? Learning to Rewrite Questions-in-Context](https://aclanthology.org/D19-1605) (Elgohary et al., EMNLP-IJCNLP 2019)

• Can You Unpack That? Learning to Rewrite Questions-in-Context (Elgohary et al., EMNLP-IJCNLP 2019)

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.