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.- Anthology ID:
- 2021.findings-emnlp.208
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2428–2434
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.208
- DOI:
- 10.18653/v1/2021.findings-emnlp.208
- Cite (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.
- Cite (Informal):
- Attention-based Contrastive Learning for Winograd Schemas (Klein & Nabi, Findings 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2021.findings-emnlp.208.pdf
- Code
- sap-samples/emnlp2021-attention-contrastive-learning
- Data
- WSC, WinoGrande