  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.
