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        <title>A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers</title>
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        <namePart type="given">Shen-yun</namePart>
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    <name type="personal">
        <namePart type="given">Chao-Chun</namePart>
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        <namePart type="given">Keh-Yih</namePart>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.92</identifier>
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        <date>2020-07</date>
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%0 Conference Proceedings
%T A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers
%A Miao, Shen-yun
%A Liang, Chao-Chun
%A Su, Keh-Yih
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F miao-etal-2020-diverse
%X We present ASDiv (Academia Sinica Diverse MWP Dataset), a diverse (in terms of both language patterns and problem types) English math word problem (MWP) corpus for evaluating the capability of various MWP solvers. Existing MWP corpora for studying AI progress remain limited either in language usage patterns or in problem types. We thus present a new English MWP corpus with 2,305 MWPs that cover more text patterns and most problem types taught in elementary school. Each MWP is annotated with its problem type and grade level (for indicating the level of difficulty). Furthermore, we propose a metric to measure the lexicon usage diversity of a given MWP corpus, and demonstrate that ASDiv is more diverse than existing corpora. Experiments show that our proposed corpus reflects the true capability of MWP solvers more faithfully.
%R 10.18653/v1/2020.acl-main.92
%U https://aclanthology.org/2020.acl-main.92
%U https://doi.org/10.18653/v1/2020.acl-main.92
%P 975-984

Markdown (Informal)

[A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers](https://aclanthology.org/2020.acl-main.92) (Miao et al., ACL 2020)

• A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (Miao et al., ACL 2020)

ACL

• Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020. A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 975–984, Online. Association for Computational Linguistics.