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        <title>Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments</title>
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    <name type="personal">
        <namePart type="given">Chung-Chi</namePart>
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        <namePart type="given">Hen-Hsen</namePart>
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        <namePart type="family">Takamura</namePart>
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    <name type="personal">
        <namePart type="given">Hsin-Hsi</namePart>
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    <abstract>In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16%, and the GRU-capsule model gets the best macro-averaged F1 score of 64.71%. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1635</identifier>
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%0 Conference Proceedings
%T Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments
%A Chen, Chung-Chi
%A Huang, Hen-Hsen
%A Takamura, Hiroya
%A Chen, Hsin-Hsi
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F chen-etal-2019-numeracy
%X In this paper, we attempt to answer the question of whether neural network models can learn numeracy, which is the ability to predict the magnitude of a numeral at some specific position in a text description. A large benchmark dataset, called Numeracy-600K, is provided for the novel task. We explore several neural network models including CNN, GRU, BiGRU, CRNN, CNN-capsule, GRU-capsule, and BiGRU-capsule in the experiments. The results show that the BiGRU model gets the best micro-averaged F1 score of 80.16%, and the GRU-capsule model gets the best macro-averaged F1 score of 64.71%. Besides discussing the challenges through comprehensive experiments, we also present an important application scenario, i.e., detecting exaggerated information, for the task.
%R 10.18653/v1/P19-1635
%U https://aclanthology.org/P19-1635/
%U https://doi.org/10.18653/v1/P19-1635
%P 6307-6313

Markdown (Informal)

[Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments](https://aclanthology.org/P19-1635/) (Chen et al., ACL 2019)

• Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments (Chen et al., ACL 2019)

ACL

• Chung-Chi Chen, Hen-Hsen Huang, Hiroya Takamura, and Hsin-Hsi Chen. 2019. Numeracy-600K: Learning Numeracy for Detecting Exaggerated Information in Market Comments. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 6307–6313, Florence, Italy. Association for Computational Linguistics.