Natural language inference (NLI) aims to determine the logical relationship between two sentences, such as Entailment, Contradiction, and Neutral. In recent years, deep learning models have become a prevailing approach to NLI, but they lack interpretability and explainability. In this work, we address the explainability of NLI by weakly supervised logical reasoning, and propose an Explainable Phrasal Reasoning (EPR) approach. Our model first detects phrases as the semantic unit and aligns corresponding phrases in the two sentences. Then, the model predicts the NLI label for the aligned phrases, and induces the sentence label by fuzzy logic formulas. Our EPR is almost everywhere differentiable and thus the system can be trained end to end. In this way, we are able to provide explicit explanations of phrasal logical relationships in a weakly supervised manner. We further show that such reasoning results help textual explanation generation.

翻译：自然语言推理（NLI）旨在确定两个句子之间的逻辑关系，例如蕴含、矛盾和中性。近年来，深度学习模型已成为NLI的主流方法，但它们缺乏可解释性。本文通过弱监督逻辑推理解决NLI的可解释性问题，并提出一种可解释短语推理（EPR）方法。我们的模型首先将短语检测为语义单元，并对齐两个句子中的对应短语。然后，模型预测对齐短语的NLI标签，并通过模糊逻辑公式推导句子标签。我们的EPR几乎处处可微，因此系统可以端到端训练。通过这种方式，我们能够以弱监督的方式提供短语逻辑关系的显式解释。我们进一步表明，此类推理结果有助于文本解释生成。