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    <abstract>Explanation regularisation (ER) has been introduced as a way to guide text classifiers to form their predictions relying on input tokens that humans consider plausible. This is achieved by introducing an auxiliary explanation loss that measures how well the output of an input attribution technique for the model agrees with human-annotated rationales. The guidance appears to benefit performance in out-of-domain (OOD) settings, presumably due to an increased reliance on plausible tokens. However, previous work has under-explored the impact of guidance on that reliance, particularly when reliance is measured using attribution techniques different from those used to guide the model. In this work, we seek to close this gap, and also explore the relationship between reliance on plausible features and OOD performance. We find that the connection between ER and the ability of a classifier to rely on plausible features has been overstated and that a stronger reliance on plausible tokens does not seem to be the cause for OOD improvements.</abstract>
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%0 Conference Proceedings
%T Explanation Regularisation through the Lens of Attributions
%A Ferreira, Pedro
%A Titov, Ivan
%A Aziz, Wilker
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F ferreira-etal-2025-explanation
%X Explanation regularisation (ER) has been introduced as a way to guide text classifiers to form their predictions relying on input tokens that humans consider plausible. This is achieved by introducing an auxiliary explanation loss that measures how well the output of an input attribution technique for the model agrees with human-annotated rationales. The guidance appears to benefit performance in out-of-domain (OOD) settings, presumably due to an increased reliance on plausible tokens. However, previous work has under-explored the impact of guidance on that reliance, particularly when reliance is measured using attribution techniques different from those used to guide the model. In this work, we seek to close this gap, and also explore the relationship between reliance on plausible features and OOD performance. We find that the connection between ER and the ability of a classifier to rely on plausible features has been overstated and that a stronger reliance on plausible tokens does not seem to be the cause for OOD improvements.
%U https://aclanthology.org/2025.coling-main.436/
%P 6530-6551

Markdown (Informal)

[Explanation Regularisation through the Lens of Attributions](https://aclanthology.org/2025.coling-main.436/) (Ferreira et al., COLING 2025)

• Explanation Regularisation through the Lens of Attributions (Ferreira et al., COLING 2025)

ACL

• Pedro Ferreira, Ivan Titov, and Wilker Aziz. 2025. Explanation Regularisation through the Lens of Attributions. In Proceedings of the 31st International Conference on Computational Linguistics, pages 6530–6551, Abu Dhabi, UAE. Association for Computational Linguistics.