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.

本文介绍了我们为 SemEval-2024 任务 1 所开发的系统：语义文本相关性（STR），在 Track C：跨语言下。该任务的目标是在没有直接监督（即零 - shot 跨语言转移）的情况下，检测给定目标语言中两个句子的语义相关性。为此，我们关注两个不同预训练语言模型：XLM-R 和 Furina 上的不同源语言选择策略。我们通过以下实验：1）单源转移并基于类型学相似性选择源语言，2）用两个最近邻源语言扩充英语训练数据，以及 3）多源转移，我们将所有训练语言与同一语系的语言进行比较。我们还研究了基于机器翻译的数据增强和脚本差异的影响。我们的提交在 C8（Kinyarwanda）测试集中取得了第一名。