Explaining Classes through Stable Word Attributions

Samuel Rönnqvist, Aki-Juhani Kyröläinen, Amanda Myntti, Filip Ginter, Veronika Laippala


Abstract
Input saliency methods have recently become a popular tool for explaining predictions of deep learning models in NLP. Nevertheless, there has been little work investigating methods for aggregating prediction-level explanations to the class level, nor has a framework for evaluating such class explanations been established. We explore explanations based on XLM-R and the Integrated Gradients input attribution method, and propose 1) the Stable Attribution Class Explanation method (SACX) to extract keyword lists of classes in text classification tasks, and 2) a framework for the systematic evaluation of the keyword lists. We find that explanations of individual predictions are prone to noise, but that stable explanations can be effectively identified through repeated training and explanation. We evaluate on web register data and show that the class explanations are linguistically meaningful and distinguishing of the classes.
Anthology ID:
2022.findings-acl.85
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1063–1074
Language:
URL:
https://aclanthology.org/2022.findings-acl.85
DOI:
10.18653/v1/2022.findings-acl.85
Bibkey:
Cite (ACL):
Samuel Rönnqvist, Aki-Juhani Kyröläinen, Amanda Myntti, Filip Ginter, and Veronika Laippala. 2022. Explaining Classes through Stable Word Attributions. In Findings of the Association for Computational Linguistics: ACL 2022, pages 1063–1074, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Explaining Classes through Stable Word Attributions (Rönnqvist et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.85.pdf
Software:
 2022.findings-acl.85.software.tgz
Code
 turkunlp/class-explainer