@inproceedings{yadav-schlechtweg-2025-xl,
title = "{XL}-{DUR}el: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification",
author = "Yadav, Sachin and
Schlechtweg, Dominik",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-ijcnlp.19/",
pages = "338--351",
ISBN = "979-8-89176-303-6",
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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%0 Conference Proceedings
%T XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification
%A Yadav, Sachin
%A Schlechtweg, Dominik
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-303-6
%F yadav-schlechtweg-2025-xl
%X 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.
%U https://aclanthology.org/2025.findings-ijcnlp.19/
%P 338-351
Markdown (Informal)
[XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification](https://aclanthology.org/2025.findings-ijcnlp.19/) (Yadav & Schlechtweg, Findings 2025)
ACL
- Sachin Yadav and Dominik Schlechtweg. 2025. XL-DURel: Finetuning Sentence Transformers for Ordinal Word-in-Context Classification. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 338–351, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.