WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks

Bradley Hauer, Grzegorz Kondrak


Abstract
The Word-in-Context (WiC) task has attracted considerable attention in the NLP community, as demonstrated by the popularity of the recent MCL-WiC SemEval shared task. Systems and lexical resources from word sense disambiguation (WSD) are often used for the WiC task and WiC dataset construction. In this paper, we establish the exact relationship between WiC and WSD, as well as the related task of target sense verification (TSV). Building upon a novel hypothesis on the equivalence of sense and meaning distinctions, we demonstrate through the application of tools from theoretical computer science that these three semantic classification problems can be pairwise reduced to each other, and therefore are equivalent. The results of experiments that involve systems and datasets for both WiC and WSD provide strong empirical evidence that our problem reductions work in practice.
Anthology ID:
2022.naacl-main.178
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2478–2486
Language:
URL:
https://aclanthology.org/2022.naacl-main.178
DOI:
10.18653/v1/2022.naacl-main.178
Bibkey:
Cite (ACL):
Bradley Hauer and Grzegorz Kondrak. 2022. WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2478–2486, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks (Hauer & Kondrak, NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.178.pdf
Data
WiCWiC-TSVWord Sense Disambiguation: a Unified Evaluation Framework and Empirical Comparison