@InProceedings{marresetaylor-matsuo:2017:WASSA2017,
  author    = {Marrese-Taylor, Edison  and  Matsuo, Yutaka},
  title     = {EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity},
  booktitle = {Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {233--237},
  abstract  = {In this paper we describe a deep learning system that has been designed and
	built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a
	representation learning approach based on inner attention on top of an RNN.
	Results show that our model offers good capabilities and is able to
	successfully identify emotion-bearing words to predict intensity without
	leveraging on lexicons, obtaining the 13t place among 22 shared task
	competitors.},
  url       = {http://www.aclweb.org/anthology/W17-5232}
}