  Natural Language Inference (NLI) is the task of determining whether a premise
entails a hypothesis. NLI with temporal order is a challenging task because
tense and aspect are complex linguistic phenomena involving interactions with
temporal adverbs and temporal connectives. To tackle this, temporal and
aspectual inference has been analyzed in various ways in the field of formal
semantics. However, a Japanese NLI system for temporal order based on the
analysis of formal semantics has not been sufficiently developed. We present a
logic-based NLI system that considers temporal order in Japanese based on
compositional semantics via Combinatory Categorial Grammar (CCG) syntactic
analysis. Our system performs inference involving temporal order by using
axioms for temporal relations and automated theorem provers. We evaluate our
system by experimenting with Japanese NLI datasets that involve temporal order.
We show that our system outperforms previous logic-based systems as well as
current deep learning-based models.
