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 $> 100\%$.

通过在部分监督下基于自然语言推理模型对合理性解释模型进行了优化，无需访问真实标签，提高了性能，并实现了与监督提取模型相当的结果和优于无监督方法 100% 以上的性能。