The final goal of Interpretable Semantic Textual Similarity (iSTS) is to build systems that explain which are the differences and commonalities between two sentences. The task adds an explanatory level on top of STS, formalized as an alignment between the chunks in the two input sentences, indicating the relation and similarity score of each alignment. The task provides train and test data on three datasets: news headlines, image captions and student answers. It attracted nine teams, totaling 20 runs. All datasets and the annotation guideline are freely available.

This material is based in part upon work supported a MINECO grant to the University of the Basque Country (TUNER project TIN2015-65308-C5-1-R). Aitor Gonzalez Agirre and Inigo Lopez-Gazpio are by doctoral grants from MINECO. The IXA group is funded by the Basque Government (A type Research Group).

Comunicació presentada al 10th International Workshop on Semantic Evaluation (SemEval-2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, Califòrnia.