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- W3214642742 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." @default.
- W3214642742 created "2021-11-22" @default.
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- W3214642742 date "2021-11-01" @default.
- W3214642742 modified "2023-09-27" @default.
- W3214642742 title "Attention-based Contrastive Learning for Winograd Schemas." @default.
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