Temporal Relation Classification: An XAI Perspective

Abstract

Temporal annotations are used to identify and mark up temporal information, offering definition into how it is expressed through linguistic properties in text. This study investigates various discriminative pre-trained language models of differing sizes on a temporal relation classification task. We define valid reasoning strategies based on the linguistic principles that guide commonly used temporal annotations. Using a combination of saliency-based and counterfactual explanations, we examine if the models’ decisions are in line with these strategies. Our findings suggest that the selected models do not rely on the expected linguistic cues for processing temporal information effectively.