@InProceedings{tian-EtAl:2017:SemEval,
  author    = {Tian, Junfeng  and  Zhou, Zhiheng  and  Lan, Man  and  Wu, Yuanbin},
  title     = {ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual 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     = {191--197},
  abstract  = {To address semantic similarity on multilingual and cross-lingual sentences, we
	firstly translate other foreign languages into English, and then
	feed our monolingual English system with various interactive features. Our
	system is further supported by combining with deep learning semantic similarity
	and our best run achieves the mean Pearson correlation 73.16\% in primary
	track.},
  url       = {http://www.aclweb.org/anthology/S17-2028}
}