Learning Inference Rules with Neural TP-Reasoner - Chen, Kezhen; Huang, Qiuyuan; Smolensky, Paul; Forbus, Kenneth; Gao, Jianfeng (NeurIPS 2020, workshop, 2020)
Most standard deep learning models do not perform logical rule-based reasoning like human and are hard to understand. We present a novel neural architecture, Tensor Product Reasoner (TP-Reasoner), for learning inference rules represented with a structured representation. In TP-Reasoner, we aim to integrate symbolic inference and deep learning: we utilize the ability of Tensor Product Representation in a neural model for learning and reasoning inference rules, which extracts intermediate representations of logical rules from a knowledge base reasoning task. TP-Reasoner achieves comparable results with baseline models. Analysis of learned inference rules in TP-Reasoner shows the interpretability of logical composition via a strong neuro-symbolic representation, a novel model expressivity, and an explicit tensor product expressions.