By explaining how humans would solve a given task, human rationales can provide strong learning signal for neural language models (NLMs). Explanation regularization (ER) aims to improve NLM generalization by pushing the NLM’s machine rationales (Which input tokens did the NLM focus on?) to align with human rationales (Which input tokens would humans focus on). Though prior works primarily study ER via in-distribution (ID) evaluation, out-of-distribution (OOD) generalization is often more critical in real-world scenarios, yet ER’s effect on OOD generalization has been underexplored.In this paper, we introduce ER-Test, a framework for evaluating ER models’ OOD generalization along three dimensions: unseen datasets, contrast set tests, and functional tests. Using ER-Test, we comprehensively analyze how ER models’ OOD generalization varies with the rationale alignment criterion (loss function), human rationale type (instance-level v/s task-level), number and choice of rationale-annotated instances, and time budget for rationale annotation. Across two tasks and six datasets, we show that ER has little impact on ID performance but yields large OOD performance gains, with the best ER criterion being task-dependent. Also, ER can improve OOD performance even with task-level or few human rationales. Finally, we find that rationale annotation is more time-efficient than label annotation for improving OOD performance. Our results with ER-Test help demonstrate ER’s utility and establish best practices for using ER effectively.

An extractive rationale explains a language model’s (LM’s) prediction on a given task instance by highlighting the text inputs that most influenced the prediction. Ideally, rationale extraction should be faithful (reflective of LM’s actual behavior) and plausible (convincing to humans), without compromising the LM’s (i.e., task model’s) task performance. Although attribution algorithms and select-predict pipelines are commonly used in rationale extraction, they both rely on certain heuristics that hinder them from satisfying all three desiderata. In light of this, we propose UNIREX, a flexible learning framework which generalizes rationale extractor optimization as follows: (1) specify architecture for a learned rationale extractor; (2) select explainability objectives (i.e., faithfulness and plausibility criteria); and (3) jointly the train task model and rationale extractor on the task using selected objectives. UNIREX enables replacing prior works’ heuristic design choices with a generic learned rationale extractor in (1) and optimizing it for all three desiderata in (2)-(3). To facilitate comparison between methods w.r.t. multiple desiderata, we introduce the Normalized Relative Gain (NRG) metric. Across five English text classification datasets, our best UNIREX configuration outperforms the strongest baselines by an average of 32.9% NRG. Plus, we find that UNIREX-trained rationale extractors’ faithfulness can even generalize to unseen datasets and tasks.