Logical Inferences with Comparatives and Generalized Quantifiers

TL;DR

This paper develops a compositional semantics and inference system for comparatives and quantifiers in English, improving logical inference in NLI tasks involving complex linguistic structures.

Contribution

It introduces a novel CCG-based semantic parsing and theorem proving approach for comparatives and quantifiers in NLI, surpassing prior logic and deep learning models.

Findings

Outperforms previous logic-based NLI systems

Outperforms recent deep learning models

Effective handling of complex comparatives and quantifiers

Abstract

Comparative constructions pose a challenge in Natural Language Inference (NLI), which is the task of determining whether a text entails a hypothesis. Comparatives are structurally complex in that they interact with other linguistic phenomena such as quantifiers, numerals, and lexical antonyms. In formal semantics, there is a rich body of work on comparatives and gradable expressions using the notion of degree. However, a logical inference system for comparatives has not been sufficiently developed for use in the NLI task. In this paper, we present a compositional semantics that maps various comparative constructions in English to semantic representations via Combinatory Categorial Grammar (CCG) parsers and combine it with an inference system based on automated theorem proving. We evaluate our system on three NLI datasets that contain complex logical inferences with comparatives, generalized quantifiers, and numerals. We show that the system outperforms previous logic-based systems as well as recent deep learning-based models.