COLING 2022main138 citations
SHAP-Based Explanation Methods: A Review for NLP Interpretability
Edoardo Mosca, Ferenc Szigeti, Stella Tragianni, Daniel Gallagher, Georg Groh
Abstract
Model explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models. The SHapley Additive exPlanations (SHAP) framework is considered by many to be a gold standard for local explanations thanks to its solid theoretical background and general applicability. In the years following its publication, several variants appeared in the literature—presenting adaptations in the core assumptions and target applications. In this work, we review all relevant SHAP-based interpretability approaches available to date and provide instructive examples as well as recommendations regarding their applicability to NLP use cases.
BibTeX
@inproceedings{mosca-etal-2022-shap,
title = "{SHAP}-Based Explanation Methods: A Review for {NLP} Interpretability",
author = "Mosca, Edoardo and
Szigeti, Ferenc and
Tragianni, Stella and
Gallagher, Daniel and
Groh, Georg",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.406/",
pages = "4593--4603"
}