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        <title>Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy</title>
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        <namePart type="given">Diana</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
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    <abstract>Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader’s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.</abstract>
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%0 Conference Proceedings
%T Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy
%A Galván-Sosa, Diana
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F galvan-sosa-2019-active
%X Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader’s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.
%R 10.18653/v1/P19-2014
%U https://aclanthology.org/P19-2014
%U https://doi.org/10.18653/v1/P19-2014
%P 106-112

Markdown (Informal)

[Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy](https://aclanthology.org/P19-2014) (Galván-Sosa, ACL 2019)

• Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy (Galván-Sosa, ACL 2019)

ACL

• Diana Galván-Sosa. 2019. Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 106–112, Florence, Italy. Association for Computational Linguistics.