@inproceedings{marrese-taylor-matsuo-2017-emoatt,
    title = "{E}mo{A}tt at {E}mo{I}nt-2017: Inner attention sentence embedding for Emotion Intensity",
    author = "Marrese-Taylor, Edison  and
      Matsuo, Yutaka",
    editor = "Balahur, Alexandra  and
      Mohammad, Saif M.  and
      van der Goot, Erik",
    booktitle = "Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-5232/",
    doi = "10.18653/v1/W17-5232",
    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."
}