@InProceedings{arroyofernandez-mezaruiz:2017:SemEval,
  author    = {Arroyo-Fern\'{a}ndez, Ignacio  and  Meza Ruiz, Ivan Vladimir},
  title     = {LIPN-IIMAS at SemEval-2017 Task 1: Subword Embeddings, Attention Recurrent Neural Networks and Cross Word Alignment for Semantic Textual Similarity},
  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     = {208--212},
  abstract  = {In this paper we report our attempt to use, on the one hand, state-of-the-art
	neural approaches that are proposed to measure Semantic Textual Similarity
	(STS). On the other hand, we propose an unsupervised cross-word alignment
	approach, which is linguistically motivated. The neural approaches proposed
	herein are divided into two main stages. The first stage deals with
	constructing neural word embeddings, the components of sentence embeddings. The
	second stage deals with constructing a semantic similarity function relating
	pairs of sentence embeddings. Unfortunately our competition results were poor
	in all tracks, therefore we concentrated our research to improve them for Track
	5 (EN-EN).},
  url       = {http://www.aclweb.org/anthology/S17-2031}
}