Skip to yearly menu bar Skip to main content

In-Person Poster presentation / poster accept

Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic

Zijun Wu · Zi Xuan Zhang · Atharva Naik · Zhijian Mei · Mauajama Firdaus · Lili Mou

MH1-2-3-4 #34

Keywords: [ Weakly Supervised Reasoning ] [ Neural Fuzzy Logic ] [ Explainability and Interpretability ] [ natural language inference ] [ Applications ]


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.

Chat is not available.