Abstract
In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers. Our approach is motivated by recent studies demonstrating that self-attention patterns in trained models contain a majority of non-linguistic regularities. We propose a computationally efficient auxiliary loss function to guide attention heads to conform to such patterns. Our method is agnostic to the actual pre-training objective and results in faster convergence of models as well as better performance on downstream tasks compared to the baselines, achieving state of the art results in low-resource settings. Surprisingly, we also find that linguistic properties of attention heads are not necessarily correlated with language modeling performance.- Anthology ID:
- 2020.findings-emnlp.419
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4676–4686
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.419
- DOI:
- 10.18653/v1/2020.findings-emnlp.419
- Cite (ACL):
- Ameet Deshpande and Karthik Narasimhan. 2020. Guiding Attention for Self-Supervised Learning with Transformers. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4676–4686, Online. Association for Computational Linguistics.
- Cite (Informal):
- Guiding Attention for Self-Supervised Learning with Transformers (Deshpande & Narasimhan, Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.419.pdf
- Code
- ameet-1997/AttentionGuidance
- Data
- GLUE, MultiNLI, QNLI