@InProceedings{zhou-huang-ji:2017:Cupral,
  author    = {zhou, zhihao  and  Huang, Lifu  and  Ji, Heng},
  title     = {Learning Phrase Embeddings from Paraphrases with GRUs},
  booktitle = {Proceedings of the First Workshop on Curation and Applications of Parallel and Comparable Corpora},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {16--23},
  abstract  = {Learning phrase representations has been widely explored in many Natural
	Language Processing tasks (e.g., Sentiment Analysis, Machine Translation) and
	has shown promising improvements. Previous studies either learn
	non-compositional phrase representations with general word embedding learning
	techniques or learn compositional phrase representations based on syntactic
	structures, which either require huge amounts of human annotations or cannot be
	easily generalized to all phrases. In this work, we propose to take advantage
	of large-scaled paraphrase database and present a pairwise-GRU framework to
	generate compositional phrase representations. Our framework can be re-used to
	generate representations for any phrases. Experimental results show that our
	framework achieves state-of-the-art results on several phrase similarity tasks.},
  url       = {http://www.aclweb.org/anthology/W17-5603}
}