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    <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.</abstract>
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%0 Conference Proceedings
%T EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
%A Marrese-Taylor, Edison
%A Matsuo, Yutaka
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y van der Goot, Erik
%S Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F marrese-taylor-matsuo-2017-emoatt
%X 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.
%R 10.18653/v1/W17-5232
%U https://aclanthology.org/W17-5232
%U https://doi.org/10.18653/v1/W17-5232
%P 233-237

Markdown (Informal)

[EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity](https://aclanthology.org/W17-5232) (Marrese-Taylor & Matsuo, WASSA 2017)

• EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity (Marrese-Taylor & Matsuo, WASSA 2017)

ACL

• Edison Marrese-Taylor and Yutaka Matsuo. 2017. EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 233–237, Copenhagen, Denmark. Association for Computational Linguistics.