@InProceedings{cer-EtAl:2017:SemEval,
  author    = {Cer, Daniel  and  Diab, Mona  and  Agirre, Eneko  and  Lopez-Gazpio, Inigo  and  Specia, Lucia},
  title     = {SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1--14},
  abstract  = {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 \emph{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 {\em 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).},
  url       = {http://www.aclweb.org/anthology/S17-2001}
}