# WiC-TSV: An Evaluation Benchmark for Target Sense Verification of Words in Context

arXiv: https://arxiv.org/abs/2004.15016 · HTML: https://mlsanity.com/paper/2004.15016/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media](https://mlsanity.com/paper/2209.07216/) (2022)
- [MirrorWiC: On Eliciting Word-in-Context Representations from Pretrained Language Models](https://mlsanity.com/paper/2109.09237/) (2021)
- [SALMA: Arabic Sense-Annotated Corpus and WSD Benchmarks](https://mlsanity.com/paper/2310.19029/) (2023)
- [Large Scale Substitution-based Word Sense Induction](https://mlsanity.com/paper/2110.07681/) (2021)
- [Context-Gloss Augmentation for Improving Arabic Target Sense Verification](https://mlsanity.com/paper/2302.03126/) (2023)
- [SenWiCh: Sense-Annotation of Low-Resource Languages for WiC using Hybrid Methods](https://mlsanity.com/paper/2505.23714/) (2025)
- [WiC = TSV = WSD: On the Equivalence of Three Semantic Tasks](https://mlsanity.com/paper/2107.14352/) (2021)
- [CoLLEGe: Concept Embedding Generation for Large Language Models](https://mlsanity.com/paper/2403.15362/) (2024)
- [Parmesan: mathematical concept extraction for education](https://mlsanity.com/paper/2307.06699/) (2023)
- [Measuring Context-Word Biases in Lexical Semantic Datasets](https://mlsanity.com/paper/2112.06733/) (2021)
- [Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025](https://mlsanity.com/paper/2606.02255/) (2026)
- [WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset](https://mlsanity.com/paper/2308.03582/) (2023)
- [WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution](https://mlsanity.com/paper/2609.20593/) (2026)

## Abstract (arXiv)

We present WiC-TSV, a new multi-domain evaluation benchmark for Word Sense Disambiguation. More specifically, we introduce a framework for Target Sense Verification of Words in Context which grounds its uniqueness in the formulation as a binary classification task thus being independent of external sense inventories, and the coverage of various domains. This makes the dataset highly flexible for the evaluation of a diverse set of models and systems in and across domains. WiC-TSV provides three different evaluation settings, depending on the input signals provided to the model. We set baseline performance on the dataset using state-of-the-art language models. Experimental results show that even though these models can perform decently on the task, there remains a gap between machine and human performance, especially in out-of-domain settings. WiC-TSV data is available at https://competitions.codalab.org/competitions/23683