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

Edison Marrese-Taylor, Yutaka Matsuo

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 13th place among 22 shared task competitors.

Knowledge Graph



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