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。以往共享任务主要关注语义相似性，而本研究则针对14种语言更广泛的语义关联现象展开探究：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、印度尼西亚语、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。这些语言源自五个不同语系，主要分布于非洲和亚洲——这些地区的自然语言处理资源相对匮乏。数据集中的每个实例均为一个句子对，并附带代表两句子间语义文本关联程度的分数。参赛系统需在三大主要赛道中，对14种语言的句子对按其语义接近程度（即语义关联度）进行排序：(a) 有监督、(b) 无监督、(c) 跨语言。该任务共吸引163名参与者，我们收到来自51个团队的70份提交（涵盖所有赛道）及38篇系统描述论文。我们报告了三个赛道中表现最优的系统，以及最常见和最高效的处理方法。