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。尽管早期的共享任务主要关注语义相似性，但本研究探讨了更广泛的语义关联现象，涵盖14种语言：阿非利卡语、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。这些语言源自五个不同的语系，主要分布在非洲和亚洲地区——这些地区的特点是自然语言处理（NLP）资源相对有限。数据集中的每个实例是一个句子对，并附有代表两个句子之间语义文本关联程度的评分。参赛系统被要求在三个主要赛道中，对这14种语言中的句子对按其语义关联程度（即意义接近程度）进行排序：（a）有监督赛道、（b）无监督赛道和（c）跨语言赛道。该任务吸引了163名参与者。我们共收到来自51个团队的70份提交（涵盖所有赛道）以及38篇系统描述论文。本文报告了表现最佳的系统以及三个赛道中最常见和最有效的方法。