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    <abstract>Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at http://sequential.qanta.org.</abstract>
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%0 Conference Proceedings
%T A dataset and baselines for sequential open-domain question answering
%A Elgohary, Ahmed
%A Zhao, Chen
%A Boyd-Graber, Jordan
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F elgohary-etal-2018-dataset
%X Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at http://sequential.qanta.org.
%R 10.18653/v1/D18-1134
%U https://aclanthology.org/D18-1134
%U https://doi.org/10.18653/v1/D18-1134
%P 1077-1083

Markdown (Informal)

[A dataset and baselines for sequential open-domain question answering](https://aclanthology.org/D18-1134) (Elgohary et al., EMNLP 2018)

• A dataset and baselines for sequential open-domain question answering (Elgohary et al., EMNLP 2018)

ACL

• Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. 2018. A dataset and baselines for sequential open-domain question answering. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1077–1083, Brussels, Belgium. Association for Computational Linguistics.