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.

提出了一个融合符号与深度学习方法的推理框架 NeuralLog，通过结合单调性逻辑推理引擎和神经网络语言模型进行短语对齐，并使用 beam search 算法解决 NLI 任务，实验证明该联合推理系统在 NLI 任务上提高了准确性，并可在 SICK 和 MED 数据集上实现最先进的准确性。