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Natural Language Inference (NLI) is the task of determining whether a premiseentails a hypothesis. NLI with temporal order is a challenging task becausetense and aspect are complex linguistic phenomena involving interactions withtemporal adverbs and temporal connectives. To tackle this, temporal andaspectual inference has been analyzed in various ways in the field of formalsemantics. However, a Japanese NLI system for temporal order based on theanalysis of formal semantics has not been sufficiently developed. We present alogic-based NLI system that considers temporal order in Japanese based oncompositional semantics via Combinatory Categorial Grammar (CCG) syntacticanalysis. Our system performs inference involving temporal order by usingaxioms for temporal relations and automated theorem provers. We evaluate oursystem by experimenting with Japanese NLI datasets that involve temporal order.We show that our system outperforms previous logic-based systems as well ascurrent deep learning-based models.

Compositional Semantics and Inference System for Temporal Order based on Japanese CCG