  Saliency post-hoc explainability methods are important tools for
understanding increasingly complex NLP models. While these methods can reflect
the model's reasoning, they may not align with human intuition, making the
explanations not plausible. In this work, we present a methodology for
incorporating rationales, which are text annotations explaining human
decisions, into text classification models. This incorporation enhances the
plausibility of post-hoc explanations while preserving their faithfulness. Our
approach is agnostic to model architectures and explainability methods. We
introduce the rationales during model training by augmenting the standard
cross-entropy loss with a novel loss function inspired by contrastive learning.
By leveraging a multi-objective optimization algorithm, we explore the
trade-off between the two loss functions and generate a Pareto-optimal frontier
of models that balance performance and plausibility. Through extensive
experiments involving diverse models, datasets, and explainability methods, we
demonstrate that our approach significantly enhances the quality of model
explanations without causing substantial (sometimes negligible) degradation in
the original model's performance.
