Combining Event Semantics and Degree Semantics for Natural Language Inference

TL;DR

This paper develops a logic-based natural language inference system that integrates event semantics and degree semantics, demonstrating effective handling of complex linguistic phenomena and outperforming existing systems.

Contribution

It introduces a novel combined semantic framework for NLI, uniting event and degree semantics within a logic-based system, and evaluates its effectiveness on challenging datasets.

Findings

Achieves high accuracy on linguistically complex NLI datasets

Outperforms previous logic-based and deep-learning systems

Shows compatibility of event and degree semantics in NLI

Abstract

In formal semantics, there are two well-developed semantic frameworks: event semantics, which treats verbs and adverbial modifiers using the notion of event, and degree semantics, which analyzes adjectives and comparatives using the notion of degree. However, it is not obvious whether these frameworks can be combined to handle cases in which the phenomena in question are interacting with each other. Here, we study this issue by focusing on natural language inference (NLI). We implement a logic-based NLI system that combines event semantics and degree semantics and their interaction with lexical knowledge. We evaluate the system on various NLI datasets containing linguistically challenging problems. The results show that the system achieves high accuracies on these datasets in comparison with previous logic-based systems and deep-learning-based systems. This suggests that the two semantic frameworks can be combined consistently to handle various combinations of linguistic phenomena without compromising the advantage of either framework.