In semantic textual similarity (STS), systems rate the degree of semantic equivalence between two text snippets. This year, the participants were challenged with new datasets in English and Spanish. The annotations for both subtasks leveraged crowdsourcing. The English subtask attracted 29 teams with 74 system runs, and the Spanish subtask engaged 7 teams participating with 16 system runs. In addition, this year we ran a pilot task on interpretable STS, where the systems needed to add an

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... tory layer, that is, they had to align the chunks in the sentence pair, explicitly annotating the kind of relation and the score of the chunk pair. The train and test data were manually annotated by an expert, and included headline and image sentence pairs from previous years. 7 teams participated with 29 runs.