Abstract

Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI). And at this time, symbolic approaches to NLI are receiving less attention. Both approaches (symbolic and DL) have their advantages and weaknesses. However, currently, no method combines them in a system to solve the task of NLI. To merge symbolic and deep learning methods, we propose an inference framework called NeuralLog, which utilizes both a monotonicity-based logical inference engine and a neural network language model for phrase alignment. Our framework models the NLI task as a classic search problem and uses the beam search algorithm to search for optimal inference paths. Experiments show that our joint logic and neural inference system improves accuracy on the NLI task and can achieve state-of-art accuracy on the SICK and MED datasets.

Highlights

  • Many Natural Language Inference (NLI) benchmarks’ state-of-the-art systems are exclusively deep learning (DL) based language models (Devlin et al, 2019; Lan et al, 2020; Liu et al, 2020; Yin and Schütze, 2017)

  • Compare to Hu et al (2020) + BERT, which explores a way of combining logic-based methods and deep learning based methods, our system

  • The main method is using a search engine and an alignment based controller to dispatch the two inference methods to their area of expertise. This way, logicbased modules can solve inference that requires logical rules and deep-learning based modules can solve inferences that contain syntactic variations which are easier for neural networks

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Summary

Introduction

Many NLI benchmarks’ state-of-the-art systems are exclusively deep learning (DL) based language models (Devlin et al, 2019; Lan et al, 2020; Liu et al, 2020; Yin and Schütze, 2017). There are logic-based systems that use symbolic reasoning and semantic formalism to solve NLI (Abzianidze, 2017; Martínez-Gómez et al, 2017; Yanaka et al, 2018; Hu et al, 2020) These systems show high precision on complex inferences involving difficult linguistic phenomena and present logical and explainable reasoning processes. These systems lack background knowledge and do not handle sentences with syntactic variations well, which makes them poor competitors with state-ofthe-art DL models. Both DL and logic-based systems show a major issue with NLI models: they are too one-dimensional (either purely DL or purely logic), and no method has combined these two ap-

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