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.
Abstract (translated)
我们提出了XL-DURel，这是一个针对序数词义相似度（Word-in-Context, WiC）分类任务进行了微调的多语言Sentence Transformer模型。我们在回归和排序任务中测试了几种损失函数，并且在处理有序数据和二元数据时，通过基于复数空间中的角度距离进行排名的目标优于之前的模型。此外，我们还表明，二元WiC可以被视为序数WiC的一个特例，并且针对一般序数任务优化的模型可以在更具体的二元任务上提高性能。这为在不同任务设定下统一处理WiC建模铺平了道路。
URL
https://arxiv.org/abs/2507.14578
