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        <title>ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers</title>
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    <abstract>We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call this dataset ConditionalQA. In addition to conditional answers, the dataset also features:(1) long context documents with information that is related in logically complex ways;(2) multi-hop questions that require compositional logical reasoning;(3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions;(4) questions asked without knowing the answers. We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions. We believe that this dataset will motivate further research in answering complex questions over long documents.</abstract>
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%0 Conference Proceedings
%T ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
%A Sun, Haitian
%A Cohen, William
%A Salakhutdinov, Ruslan
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F sun-etal-2022-conditionalqa
%X We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call this dataset ConditionalQA. In addition to conditional answers, the dataset also features:(1) long context documents with information that is related in logically complex ways;(2) multi-hop questions that require compositional logical reasoning;(3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions;(4) questions asked without knowing the answers. We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions. We believe that this dataset will motivate further research in answering complex questions over long documents.
%R 10.18653/v1/2022.acl-long.253
%U https://aclanthology.org/2022.acl-long.253
%U https://doi.org/10.18653/v1/2022.acl-long.253
%P 3627-3637

Markdown (Informal)

[ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers](https://aclanthology.org/2022.acl-long.253) (Sun et al., ACL 2022)

• ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers (Sun et al., ACL 2022)

ACL

• Haitian Sun, William Cohen, and Ruslan Salakhutdinov. 2022. ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3627–3637, Dublin, Ireland. Association for Computational Linguistics.