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        <title>Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study</title>
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    <name type="personal">
        <namePart type="given">Xiangyang</namePart>
        <namePart type="family">Mou</namePart>
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    <name type="personal">
        <namePart type="given">Chenghao</namePart>
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    <name type="personal">
        <namePart type="given">Bingsheng</namePart>
        <namePart type="family">Yao</namePart>
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    <name type="personal">
        <namePart type="given">Xiaoxiao</namePart>
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    <abstract>Recent advancements in open-domain question answering (ODQA), that is, finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets. However, progress in QA over book stories (Book QA) lags despite its similar task formulation to ODQA. This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. This quantifies the challenges Book QA poses, as well as advances the published state-of-the-art with a ∼7% absolute improvement on ROUGE-L. (2) We further analyze the detailed challenges in Book QA through human studies.1 Our findings indicate that the event-centric questions dominate this task, which exemplifies the inability of existing QA models to handle event-oriented scenarios.</abstract>
    <identifier type="citekey">mou-etal-2021-narrative</identifier>
    <identifier type="doi">10.1162/tacl_a_00411</identifier>
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%0 Journal Article
%T Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study
%A Mou, Xiangyang
%A Yang, Chenghao
%A Yu, Mo
%A Yao, Bingsheng
%A Guo, Xiaoxiao
%A Potdar, Saloni
%A Su, Hui
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F mou-etal-2021-narrative
%X Recent advancements in open-domain question answering (ODQA), that is, finding answers from large open-domain corpus like Wikipedia, have led to human-level performance on many datasets. However, progress in QA over book stories (Book QA) lags despite its similar task formulation to ODQA. This work provides a comprehensive and quantitative analysis about the difficulty of Book QA: (1) We benchmark the research on the NarrativeQA dataset with extensive experiments with cutting-edge ODQA techniques. This quantifies the challenges Book QA poses, as well as advances the published state-of-the-art with a ∼7% absolute improvement on ROUGE-L. (2) We further analyze the detailed challenges in Book QA through human studies.1 Our findings indicate that the event-centric questions dominate this task, which exemplifies the inability of existing QA models to handle event-oriented scenarios.
%R 10.1162/tacl_a_00411
%U https://aclanthology.org/2021.tacl-1.61
%U https://doi.org/10.1162/tacl_a_00411
%P 1032-1046

Markdown (Informal)

[Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study](https://aclanthology.org/2021.tacl-1.61) (Mou et al., TACL 2021)

• Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study (Mou et al., TACL 2021)

ACL

• Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, and Hui Su. 2021. Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study. Transactions of the Association for Computational Linguistics, 9:1032–1046.