  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.
