The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with the extracted information. Their primary objective is to provide precise and faithful explanations, represented by the extracted rationales. In this paper, we take a semi-supervised approach to optimize for the plausibility of extracted rationales. We adopt a pre-trained natural language inference (NLI) model and further fine-tune it on a small set of supervised rationales ($10\%$). The NLI predictor is leveraged as a source of supervisory signals to the explainer via entailment alignment. We show that, by enforcing the alignment agreement between the explanation and answer in a question-answering task, the performance can be improved without access to ground truth labels. We evaluate our approach on the ERASER dataset and show that our approach achieves comparable results with supervised extractive models and outperforms unsupervised approaches by $> 100\%$.

翻译：随着复杂且不透明的黑箱模型日益普及，采用可解释性措施变得至关重要，其中抽取式解释模型作为一种更具可解释性的选择。这类模型（也称为“先解释后预测”模型）利用解释器提取解释，随后用提取出的信息作为预测器的条件。其主要目标是提供精确且忠实的解释，由提取的解释片段体现。在本文中，我们采用半监督方法来优化提取解释的可信度。我们采用预训练的自然语言推理（NLI）模型，并在少量有监督解释（10%）上进一步微调。通过蕴含对齐，将NLI预测器作为解释器的监督信号源。我们证明，在问答任务中强制解释与答案之间的对齐一致性，可以在缺乏真实标签的情况下提升性能。在ERASER数据集上的评估表明，我们的方法取得了与有监督抽取式模型相当的结果，并比无监督方法提升超过100%。