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        <title>Knowledge-Aware Meta-learning for Low-Resource Text Classification</title>
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    <name type="personal">
        <namePart type="given">Huaxiu</namePart>
        <namePart type="family">Yao</namePart>
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    <name type="personal">
        <namePart type="given">Ying-xin</namePart>
        <namePart type="family">Wu</namePart>
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    <name type="personal">
        <namePart type="given">Maruan</namePart>
        <namePart type="family">Al-Shedivat</namePart>
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    <name type="personal">
        <namePart type="given">Eric</namePart>
        <namePart type="family">Xing</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.136</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Knowledge-Aware Meta-learning for Low-Resource Text Classification
%A Yao, Huaxiu
%A Wu, Ying-xin
%A Al-Shedivat, Maruan
%A Xing, Eric
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F yao-etal-2021-knowledge
%X Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.
%R 10.18653/v1/2021.emnlp-main.136
%U https://aclanthology.org/2021.emnlp-main.136
%U https://doi.org/10.18653/v1/2021.emnlp-main.136
%P 1814-1821

Markdown (Informal)

[Knowledge-Aware Meta-learning for Low-Resource Text Classification](https://aclanthology.org/2021.emnlp-main.136) (Yao et al., EMNLP 2021)

• Knowledge-Aware Meta-learning for Low-Resource Text Classification (Yao et al., EMNLP 2021)

ACL

• Huaxiu Yao, Ying-xin Wu, Maruan Al-Shedivat, and Eric Xing. 2021. Knowledge-Aware Meta-learning for Low-Resource Text Classification. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 1814–1821, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.