EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity

• Edison Marrese-Taylor ^[1] ; Yutaka Matsuo ^[1]
  1. [1] University of Tokyo

     University of Tokyo

     Japón

     
• Localización: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop / Alexandra Balahur Dobrescu (ed. lit.), Saif M. Mohammad (ed. lit.), Erik van der Goot (ed. lit.), 2017, ISBN 978-1-945626-95-1, págs. 233-237
• Idioma: inglés
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• Resumen

  • 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 representa- tion learning approach based on inner at- tention 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 13th place among 22 shared task competitors.