2023 Volume 30 Issue 2 Pages 632-663
Natural language inference (NLI) is the task of identifying the inferential relation between a text pair. In recent times, neural-based approaches have achieved high performance in NLI. However, they are unable to explain their reasoning processes. On the other hand, symbolic approaches have the advantage that their reasoning process is understandable to humans. This paper proposes a method for integrating a neural NLI model and the tableau proof system, with the latter explaining the reasoning processes. The standard tableau method decomposes logical formulas by applying inferential rules and checks for a valuation that satisfies the given constraints. Unlike the standard tableau method, the proposed method uses dependency structures as its components rather than logical formulas and employs a neural NLI model for the latter process. To analyze the behavior of our method, we conducted an experiment on the neural NLI model and the proposed method using SNLI corpus. In addition, we formalize our method model-theoretically and clarify the theoretical limitations of this method based on the formalization.
