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We present the system developed at FBK for the SemEval 2016 Shared Task 2 ”Interpretable Semantic Textual Similarity” as well as the results of the submitted runs. We use a single neural network classification model for predicting the alignment at chunk level, the relation type of the alignment and the similarity scores. Our best run was ranked as first in one the subtracks (i.e. raw input data, Student Answers), among 12 runs submitted, and the approach proved to be very robust across the different datasets.

FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity