WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks
This work clarifies foundational relationships in NLP semantics, potentially unifying approaches across tasks, though it is incremental in building on existing tools and hypotheses.
The paper tackles the problem of establishing the relationship between three semantic tasks—Word-in-Context (WiC), word sense disambiguation (WSD), and target sense verification (TSV)—by demonstrating through theoretical reductions and empirical experiments that they are equivalent.
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.