XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification

Sachin Yadav; Dominik Schlechtweg

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arXiv · 2507.14578

XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification

Sachin Yadav·Dominik Schlechtweg

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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.

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BibTeX RIS

Sachin Yadav, Dominik Schlechtweg. 2025-07-19. XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification. https://arxiv.org/abs/2507.14578

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