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WiC task

The Word-in-Context task, commonly abbreviated as the WiC task, is a natural language processing benchmark designed to evaluate how effectively computational models capture variations in word meaning across different textual environments. Formulated as a binary classification problem, the task presents a model with a single target word appearing in two distinct context sentences and requires the model to determine whether the word shares the same meaning in both instances. Unlike traditional word sense disambiguation approaches that map words to explicit sense inventories or predefined dictionary entries, the WiC task directly evaluates the ability of contextualized representations to detect semantic shifts based solely on surrounding context. As a result, it serves as a standard framework for testing contextualized word embeddings, transformer-based language models, and systems designed for lexical semantic analysis.

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XL-LEXEME: WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE

XL-LEXEME: WiC Pretrained Model for Cross-Lingual LEXical sEMantic changE

Pierluigi Cassotti, Lucia Siciliani, Marco de Gemmis, Giovanni Semeraro, Pierpaolo Basile

OrganizationsUniversity of Bari

Why you should read this

Presents XL-LEXEME, a bi-encoder model that adapts Sentence-BERT to the Word-in-Context task with target-word highlighting to generate scalable, comparable lexical representations that achieve state-of-the-art semantic change detection across multiple languages.

The recent introduction of large-scale datasets for the WiC (Word in Context) task enables the creation of more reliable and meaningful contextualized word embeddings. However, most of the approaches to the WiC task use cross-encoders, which prevent the possibility of deriving comparable word embeddings. In this work, we introduce XL-LEXEME, a Lexical Semantic Change Detection model. XL-LEXEME extends SBERT, highlighting the target word in the sentence. We evaluate XL-LEXEME on the multilingual benchmarks for SemEval-2020 Task 1 - Lexical Semantic Change (LSC) Detection and the RuShiftEval shared task involving five languages: English, German, Swedish, Latin, and Russian. XL-LEXEME outperforms the state-of-the-art in English, German and Swedish with statistically significant differences from the baseline results and obtains state-of-the-art performance in the RuShiftEval shared task.

Added

2026-10-02