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        <title>Attention-based Contrastive Learning for Winograd Schemas</title>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</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>Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This paper investigates whether contrastive learning can be extended to Transfomer attention to tackling the Winograd Schema Challenge. To this end, we propose a novel self-supervised framework, leveraging a contrastive loss directly at the level of self-attention. Experimental analysis of our attention-based models on multiple datasets demonstrates superior commonsense reasoning capabilities. The proposed approach outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.208</identifier>
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%0 Conference Proceedings
%T Attention-based Contrastive Learning for Winograd Schemas
%A Klein, Tassilo
%A Nabi, Moin
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F klein-nabi-2021-attention-based
%X Self-supervised learning has recently attracted considerable attention in the NLP community for its ability to learn discriminative features using a contrastive objective. This paper investigates whether contrastive learning can be extended to Transfomer attention to tackling the Winograd Schema Challenge. To this end, we propose a novel self-supervised framework, leveraging a contrastive loss directly at the level of self-attention. Experimental analysis of our attention-based models on multiple datasets demonstrates superior commonsense reasoning capabilities. The proposed approach outperforms all comparable unsupervised approaches while occasionally surpassing supervised ones.
%R 10.18653/v1/2021.findings-emnlp.208
%U https://aclanthology.org/2021.findings-emnlp.208
%U https://doi.org/10.18653/v1/2021.findings-emnlp.208
%P 2428-2434

Markdown (Informal)

[Attention-based Contrastive Learning for Winograd Schemas](https://aclanthology.org/2021.findings-emnlp.208) (Klein & Nabi, Findings 2021)

• Attention-based Contrastive Learning for Winograd Schemas (Klein & Nabi, Findings 2021)

ACL

• Tassilo Klein and Moin Nabi. 2021. Attention-based Contrastive Learning for Winograd Schemas. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2428–2434, Punta Cana, Dominican Republic. Association for Computational Linguistics.