XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912691004637184 |
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| author | Yadav, Sachin Schlechtweg, Dominik |
| author_facet | Yadav, Sachin Schlechtweg, Dominik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification Yadav, Sachin Schlechtweg, Dominik Computation and Language 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. |
| title | XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.14578 |