We present the first shared task on Semantic Textual Relatedness (STR). While earlier shared tasks primarily focused on semantic similarity, we instead investigate the broader phenomenon of semantic relatedness across 14 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Punjabi, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia -- regions characterised by the relatively limited availability of NLP resources. Each instance in the datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. Participating systems were asked to rank sentence pairs by their closeness in meaning (i.e., their degree of semantic relatedness) in the 14 languages in three main tracks: (a) supervised, (b) unsupervised, and (c) crosslingual. The task attracted 163 participants. We received 70 submissions in total (across all tasks) from 51 different teams, and 38 system description papers. We report on the best-performing systems as well as the most common and the most effective approaches for the three different tracks.

翻译：我们提出了首个关于语义文本关联性（STR）的共享任务。与早期主要聚焦语义相似性的共享任务不同，本研究在更广泛的视角下探究14种语言的语义关联现象：南非荷兰语、阿尔及利亚阿拉伯语、阿姆哈拉语、英语、豪萨语、印地语、印尼语、卢旺达语、马拉地语、摩洛哥阿拉伯语、现代标准阿拉伯语、旁遮普语、西班牙语和泰卢固语。这些语言分属五个不同语系，主要分布于非洲和亚洲——这些地区的特点是NLP资源相对有限。数据集中的每个实例均为一个句子对，并附带表示两个句子间语义文本关联程度的评分。参赛系统需针对14种语言在三个主要赛道中按语义关联度（即意义相近程度）对句子对进行排序：（a）有监督赛道、（b）无监督赛道和（c）跨语言赛道。该任务共吸引163名参赛者。我们最终收到来自51个团队的70份提交方案（涵盖所有赛道）及38篇系统描述论文。本报告将介绍三个赛道中的最佳系统方案，以及最常用且最有效的实现方法。