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        <title>Hypernymy Detection for Low-Resource Languages via Meta Learning</title>
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    <name type="personal">
        <namePart type="given">Changlong</namePart>
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        <namePart type="given">Jialong</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>Hypernymy detection, a.k.a, lexical entailment, is a fundamental sub-task of many natural language understanding tasks. Previous explorations mostly focus on monolingual hypernymy detection on high-resource languages, e.g., English, but few investigate the low-resource scenarios. This paper addresses the problem of low-resource hypernymy detection by combining high-resource languages. We extensively compare three joint training paradigms and for the first time propose applying meta learning to relieve the low-resource issue. Experiments demonstrate the superiority of our method among the three settings, which substantially improves the performance of extremely low-resource languages by preventing over-fitting on small datasets.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.336</identifier>
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%0 Conference Proceedings
%T Hypernymy Detection for Low-Resource Languages via Meta Learning
%A Yu, Changlong
%A Han, Jialong
%A Zhang, Haisong
%A Ng, Wilfred
%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 yu-etal-2020-hypernymy
%X Hypernymy detection, a.k.a, lexical entailment, is a fundamental sub-task of many natural language understanding tasks. Previous explorations mostly focus on monolingual hypernymy detection on high-resource languages, e.g., English, but few investigate the low-resource scenarios. This paper addresses the problem of low-resource hypernymy detection by combining high-resource languages. We extensively compare three joint training paradigms and for the first time propose applying meta learning to relieve the low-resource issue. Experiments demonstrate the superiority of our method among the three settings, which substantially improves the performance of extremely low-resource languages by preventing over-fitting on small datasets.
%R 10.18653/v1/2020.acl-main.336
%U https://aclanthology.org/2020.acl-main.336
%U https://doi.org/10.18653/v1/2020.acl-main.336
%P 3651-3656

Markdown (Informal)

[Hypernymy Detection for Low-Resource Languages via Meta Learning](https://aclanthology.org/2020.acl-main.336) (Yu et al., ACL 2020)

• Hypernymy Detection for Low-Resource Languages via Meta Learning (Yu et al., ACL 2020)

ACL

• Changlong Yu, Jialong Han, Haisong Zhang, and Wilfred Ng. 2020. Hypernymy Detection for Low-Resource Languages via Meta Learning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3651–3656, Online. Association for Computational Linguistics.