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EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
August 18, 2017 ยท Declared Dead ยท ๐ WASSA@EMNLP
"No code URL or promise found in abstract"
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Authors
Edison Marrese-Taylor, Yutaka Matsuo
arXiv ID
1708.05521
Category
cs.CL: Computation & Language
Citations
2
Venue
WASSA@EMNLP
Last Checked
5 months ago
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 13th place among 22 shared task competitors.
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