{"ID":2892807,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.14578","arxiv_id":"2507.14578","title":"XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification","abstract":"We propose XL-DURel, a finetuned, multilingual Sentence Transformer model optimized for ordinal Word-in-Context classification. We test several loss functions for regression and ranking tasks managing to outperform previous models on ordinal and binary data with a ranking objective based on angular distance in complex space. We further show that binary WiC can be treated as a special case of ordinal WiC and that optimizing models for the general ordinal task improves performance on the more specific binary task. This paves the way for a unified treatment of WiC modeling across different task formulations.","short_abstract":"We propose XL-DURel, a finetuned, multilingual Sentence Transformer model optimized for ordinal Word-in-Context classification. We test several loss functions for regression and ranking tasks managing to outperform previous models on ordinal and binary data with a ranking objective based on angular distance in complex...","url_abs":"https://arxiv.org/abs/2507.14578","url_pdf":"https://arxiv.org/pdf/2507.14578v2","authors":"[\"Sachin Yadav\",\"Dominik Schlechtweg\"]","published":"2025-07-19T11:40:37Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Transformer\"]","has_code":false}