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.- Anthology ID:
- 2022.coling-1.406
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4593–4603
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.406
- DOI:
- Bibkey:
- Cite (ACL):
- Edoardo Mosca, Ferenc Szigeti, Stella Tragianni, Daniel Gallagher, and Georg Groh. 2022. SHAP-Based Explanation Methods: A Review for NLP Interpretability. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4593–4603, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- SHAP-Based Explanation Methods: A Review for NLP Interpretability (Mosca et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.406.pdf
Export citation
@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",
abstract = "Model explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models. The \textit{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.",
}
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%0 Conference Proceedings %T SHAP-Based Explanation Methods: A Review for NLP Interpretability %A Mosca, Edoardo %A Szigeti, Ferenc %A Tragianni, Stella %A Gallagher, Daniel %A Groh, Georg %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %S Proceedings of the 29th International Conference on Computational Linguistics %D 2022 %8 October %I International Committee on Computational Linguistics %C Gyeongju, Republic of Korea %F mosca-etal-2022-shap %X 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. %U https://aclanthology.org/2022.coling-1.406 %P 4593-4603
Markdown (Informal)
[SHAP-Based Explanation Methods: A Review for NLP Interpretability](https://aclanthology.org/2022.coling-1.406) (Mosca et al., COLING 2022)
- SHAP-Based Explanation Methods: A Review for NLP Interpretability (Mosca et al., COLING 2022)
ACL
- Edoardo Mosca, Ferenc Szigeti, Stella Tragianni, Daniel Gallagher, and Georg Groh. 2022. SHAP-Based Explanation Methods: A Review for NLP Interpretability. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4593–4603, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.