Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic

Understand

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.