  Semantic Textual Similarity (STS) measures the meaning similarity of
sentences. Applications include machine translation (MT), summarization,
generation, question answering (QA), short answer grading, semantic search,
dialog and conversational systems. The STS shared task is a venue for assessing
the current state-of-the-art. The 2017 task focuses on multilingual and
cross-lingual pairs with one sub-track exploring MT quality estimation (MTQE)
data. The task obtained strong participation from 31 teams, with 17
participating in all language tracks. We summarize performance and review a
selection of well performing methods. Analysis highlights common errors,
providing insight into the limitations of existing models. To support ongoing
work on semantic representations, the STS Benchmark is introduced as a new
shared training and evaluation set carefully selected from the corpus of
English STS shared task data (2012-2017).
