How does BERT’s attention change when you fine-tune? An analysis methodology and a case study in negation scope

Yiyun Zhao, Steven Bethard


Abstract
Large pretrained language models like BERT, after fine-tuning to a downstream task, have achieved high performance on a variety of NLP problems. Yet explaining their decisions is difficult despite recent work probing their internal representations. We propose a procedure and analysis methods that take a hypothesis of how a transformer-based model might encode a linguistic phenomenon, and test the validity of that hypothesis based on a comparison between knowledge-related downstream tasks with downstream control tasks, and measurement of cross-dataset consistency. We apply this methodology to test BERT and RoBERTa on a hypothesis that some attention heads will consistently attend from a word in negation scope to the negation cue. We find that after fine-tuning BERT and RoBERTa on a negation scope task, the average attention head improves its sensitivity to negation and its attention consistency across negation datasets compared to the pre-trained models. However, only the base models (not the large models) improve compared to a control task, indicating there is evidence for a shallow encoding of negation only in the base models.
Anthology ID:
2020.acl-main.429
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4729–4747
Language:
URL:
https://aclanthology.org/2020.acl-main.429
DOI:
10.18653/v1/2020.acl-main.429
Bibkey:
Cite (ACL):
Yiyun Zhao and Steven Bethard. 2020. How does BERT’s attention change when you fine-tune? An analysis methodology and a case study in negation scope. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4729–4747, Online. Association for Computational Linguistics.
Cite (Informal):
How does BERT’s attention change when you fine-tune? An analysis methodology and a case study in negation scope (Zhao & Bethard, ACL 2020)
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PDF:
https://aclanthology.org/2020.acl-main.429.pdf
Video:
 http://slideslive.com/38928830