LEXPLAIN: Improving Model Explanations via Lexicon Supervision

Abstract

Model explanations that shed light on the model's predictions are becoming a desired additional output of NLP models, alongside their predictions. Challenges in creating these explanations include making them trustworthy and faithful to the model's predictions. In this work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXPLAIN, uses task-related lexicons to directly supervise model explanations. This approach consistently improves the plausibility of model's explanations without sacrificing performance on the task, as we demonstrate on sentiment analysis and toxicity detection. Our analyses show that our method also demotes spurious correlations (i.e., with respect to African American English dialect) on toxicity detection, improving fairness.