This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), on Track C: Cross-lingual. The task aims to detect semantic relatedness of two sentences in a given target language without access to direct supervision (i.e. zero-shot cross-lingual transfer). To this end, we focus on different source language selection strategies on two different pre-trained languages models: XLM-R and Furina. We experiment with 1) single-source transfer and select source languages based on typological similarity, 2) augmenting English training data with the two nearest-neighbor source languages, and 3) multi-source transfer where we compare selecting on all training languages against languages from the same family. We further study machine translation-based data augmentation and the impact of script differences. Our submission achieved the first place in the C8 (Kinyarwanda) test set.

翻译：本文介绍了我们为SemEval-2024任务1（语义文本相关性，STR）的Track C（跨语言赛道）开发的系统。该任务旨在无直接监督（即零样本跨语言迁移）的条件下，检测给定目标语言中两个句子的语义相关性。为此，我们聚焦于两种预训练语言模型（XLM-R和Furina）上的不同源语言选择策略。我们实验了三种方法：1）基于类型学相似性选择源语言的单源迁移；2）将英语训练数据与两个最近邻源语言进行增强；3）多源迁移，比较基于所有训练语言与同一语系语言的选择策略。我们进一步研究了基于机器翻译的数据增强以及文字差异的影响。我们的提交在C8（基尼亚卢旺达语）测试集中取得了第一名。