Combining Event Semantics and Degree Semantics for Natural Language Inference

Izumi Haruta, Koji Mineshima, Daisuke Bekki

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.
Anthology ID:
2020.coling-main.156
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1758–1764
Language:
URL:
https://aclanthology.org/2020.coling-main.156
DOI:
10.18653/v1/2020.coling-main.156
Bibkey:
Cite (ACL):
Izumi Haruta, Koji Mineshima, and Daisuke Bekki. 2020. Combining Event Semantics and Degree Semantics for Natural Language Inference. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1758–1764, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Combining Event Semantics and Degree Semantics for Natural Language Inference (Haruta et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2020.coling-main.156.pdf
Code
 izumi-h/ccgcomp
Data
MED, MultiNLI, SICK
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