# Logical Inferences with Comparatives and Generalized Quantifiers

arXiv: https://arxiv.org/abs/2005.07954 · HTML: https://mlsanity.com/paper/2005.07954/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2020.

## Built on by (most cited first, 5 of 5)

- [Towards Faithful Model Explanation in NLP: A Survey](https://mlsanity.com/paper/2209.11326/) (2022)
- [Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://mlsanity.com/paper/2204.10615/) (2022)
- [Compositional Semantics and Inference System for Temporal Order based on Japanese CCG](https://mlsanity.com/paper/2204.09245/) (2022)
- [Logical Inference for Counting on Semi-structured Tables](https://mlsanity.com/paper/2204.07803/) (2022)
- [Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language Inference](https://mlsanity.com/paper/2307.05034/) (2023)

## Abstract (arXiv)

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.