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    <titleInfo>
        <title>Learning Prototype Representations Across Few-Shot Tasks for Event Detection</title>
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    <name type="personal">
        <namePart type="given">Viet</namePart>
        <namePart type="family">Lai</namePart>
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        <namePart type="given">Thien</namePart>
        <namePart type="given">Huu</namePart>
        <namePart type="family">Nguyen</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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            <namePart type="family">Yih</namePart>
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    <abstract>We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/laiviet/fsl-proact.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.427</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Learning Prototype Representations Across Few-Shot Tasks for Event Detection
%A Lai, Viet
%A Dernoncourt, Franck
%A Nguyen, Thien Huu
%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 lai-etal-2021-learning
%X We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/laiviet/fsl-proact.
%R 10.18653/v1/2021.emnlp-main.427
%U https://aclanthology.org/2021.emnlp-main.427
%U https://doi.org/10.18653/v1/2021.emnlp-main.427
%P 5270-5277

Markdown (Informal)

[Learning Prototype Representations Across Few-Shot Tasks for Event Detection](https://aclanthology.org/2021.emnlp-main.427) (Lai et al., EMNLP 2021)

• Learning Prototype Representations Across Few-Shot Tasks for Event Detection (Lai et al., EMNLP 2021)

ACL

• Viet Lai, Franck Dernoncourt, and Thien Huu Nguyen. 2021. Learning Prototype Representations Across Few-Shot Tasks for Event Detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 5270–5277, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.