An Empirical Study on Explanations in Out-of-Domain Settings
George Chrysostomou, Νικόλαος Αλέτρας · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
Recent work in Natural Language Processing has focused on developing approaches that extract faithful explanations, either via identifying the most important tokens in the input (i.e.post-hoc explanations) or by designing inherently faithful models that first select the most important tokens and then use them to predict the correct label (i.e.select-then-predict models).Currently, these approaches are largely evaluated on in-domain settings.Yet, little is known about how post-hoc explanations and inherently faithful models perform in out-ofdomain settings.In this paper, we conduct an extensive empirical study that examines: (1) the out-of-domain faithfulness of post-hoc explanations, generated by five feature attribution methods; and (2) the out-of-domain performance of two inherently faithful models over six datasets.Contrary to our expectations, results show that in many cases out-of-domain post-hoc explanation faithfulness measured by sufficiency and comprehensiveness is higher compared to in-domain.We find this misleading and suggest using a random baseline as a yardstick for evaluating post-hoc explanation faithfulness.Our findings also show that selectthen predict models demonstrate comparable predictive performance in out-of-domain settings to full-text trained models. 1