利用人类理由探索文本分类器模型性能与解释合理性之间的权衡
Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales
AI总结：
本研究提出将人类理由融入文本分类模型的方法，通过新型对比损失与多目标优化探索性能与解释合理性的权衡，在不大幅降低模型性能的前提下显著提升解释质量。
摘要：
显著性事后可解释性方法是理解日益复杂的NLP模型的重要工具。尽管这些方法能够反映模型的推理过程，但它们可能与人类直觉不符，导致解释不具备合理性。在本研究中，我们提出了一种将理由（即解释人类决策的文本标注）融入文本分类模型的方法。这种融入方式在保留事后解释忠实度的同时，提升了其合理性。我们的方法与模型架构和可解释性方法无关。我们在模型训练期间引入理由，通过受对比学习启发的新型损失函数对标准交叉熵损失进行增强。借助多目标优化算法，我们探索了两种损失函数之间的权衡，并生成了平衡性能与合理性的帕累托最优模型前沿。通过涉及多种模型、数据集和可解释性方法的大量实验，我们证明了本方法能够显著提升模型解释质量，同时不会导致原始模型性能出现大幅（有时可忽略不计的）下降。
英文摘要：
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.
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