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Deep learning (DL) based language models achieve high performance on variousbenchmarks for Natural Language Inference (NLI). And at this time, symbolicapproaches to NLI are receiving less attention. Both approaches (symbolic andDL) have their advantages and weaknesses. However, currently, no methodcombines them in a system to solve the task of NLI. To merge symbolic and deeplearning methods, we propose an inference framework called NeuralLog, whichutilizes both a monotonicity-based logical inference engine and a neuralnetwork language model for phrase alignment. Our framework models the NLI taskas a classic search problem and uses the beam search algorithm to search foroptimal inference paths. Experiments show that our joint logic and neuralinference system improves accuracy on the NLI task and can achieve state-of-artaccuracy on the SICK and MED datasets.

NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning