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

2022-04-20 · ACL 2022 5 · Tomoki Sugimoto, Hitomi Yanaka

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.

📄 PDF Abstract BibTeX arXiv:2204.09245

Code (1)

ynklab/ccgtemp 공식 구현

Tasks

Natural Language Inference

Building compositional semantics and higher-order inference system for a wide-coverage Japanese CCG parser

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Higher-order logical inference with compositional semantics

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Semantic Composition

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Diversity Semantic correspondence Sentence

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We develop the compositional theory of active inference by introducing activity, functorially relating statistical games to the dynamical systems which play them, using the new notion of approximate inference doctrine. I…

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