Attention-based Contrastive Learning for Winograd Schemas

Tassilo Klein; Moin Nabi

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arXiv · 2109.05108

Attention-based Contrastive Learning for Winograd Schemas

Tassilo Klein·Moin Nabi

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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.

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Tassilo Klein, Moin Nabi. 2021-09-10. Attention-based Contrastive Learning for Winograd Schemas. https://arxiv.org/abs/2109.05108

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