GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers

Ali Modarressi, Mohsen Fayyaz, Yadollah Yaghoobzadeh, Mohammad Taher Pilehvar


Abstract
There has been a growing interest in interpreting the underlying dynamics of Transformers. While self-attention patterns were initially deemed as the primary option, recent studies have shown that integrating other components can yield more accurate explanations. This paper introduces a novel token attribution analysis method that incorporates all the components in the encoder block and aggregates this throughout layers. Through extensive quantitative and qualitative experiments, we demonstrate that our method can produce faithful and meaningful global token attributions. Our experiments reveal that incorporating almost every encoder component results in increasingly more accurate analysis in both local (single layer) and global (the whole model) settings. Our global attribution analysis significantly outperforms previous methods on various tasks regarding correlation with gradient-based saliency scores. Our code is freely available at https://github.com/mohsenfayyaz/GlobEnc.
Anthology ID:
2022.naacl-main.19
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
258–271
Language:
URL:
https://aclanthology.org/2022.naacl-main.19
DOI:
10.18653/v1/2022.naacl-main.19
Bibkey:
Cite (ACL):
Ali Modarressi, Mohsen Fayyaz, Yadollah Yaghoobzadeh, and Mohammad Taher Pilehvar. 2022. GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 258–271, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers (Modarressi et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.19.pdf
Code
 mohsenfayyaz/globenc
Data
HateXplainMultiNLISST