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        <title>PolicyQA: A Reading Comprehension Dataset for Privacy Policies</title>
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    <name type="personal">
        <namePart type="given">Wasi</namePart>
        <namePart type="family">Ahmad</namePart>
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        <namePart type="given">Jianfeng</namePart>
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    <name type="personal">
        <namePart type="given">Yuan</namePart>
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    <name type="personal">
        <namePart type="given">Kai-Wei</namePart>
        <namePart type="family">Chang</namePart>
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            <namePart type="given">Trevor</namePart>
            <namePart type="family">Cohn</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.66</identifier>
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        <date>2020-11</date>
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            <start>743</start>
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%0 Conference Proceedings
%T PolicyQA: A Reading Comprehension Dataset for Privacy Policies
%A Ahmad, Wasi
%A Chi, Jianfeng
%A Tian, Yuan
%A Chang, Kai-Wei
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ahmad-etal-2020-policyqa
%X Privacy policy documents are long and verbose. A question answering (QA) system can assist users in finding the information that is relevant and important to them. Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question. On the contrary, we argue that providing users with a short text span from policy documents reduces the burden of searching the target information from a lengthy text segment. In this paper, we present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. PolicyQA provides 714 human-annotated questions written for a wide range of privacy practices. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.
%R 10.18653/v1/2020.findings-emnlp.66
%U https://aclanthology.org/2020.findings-emnlp.66
%U https://doi.org/10.18653/v1/2020.findings-emnlp.66
%P 743-749

Markdown (Informal)

[PolicyQA: A Reading Comprehension Dataset for Privacy Policies](https://aclanthology.org/2020.findings-emnlp.66) (Ahmad et al., Findings 2020)

• PolicyQA: A Reading Comprehension Dataset for Privacy Policies (Ahmad et al., Findings 2020)

ACL

• Wasi Ahmad, Jianfeng Chi, Yuan Tian, and Kai-Wei Chang. 2020. PolicyQA: A Reading Comprehension Dataset for Privacy Policies. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 743–749, Online. Association for Computational Linguistics.