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EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
lib:4b4b8c0fd4c320bd (v1.0.0)
Authors: Edison Marrese-Taylor,Yutaka Matsuo
Where published: WS 2017 9
ArXiv: 1708.05521
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1708.05521v1
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.
Where published: WS 2017 9
ArXiv: 1708.05521
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: http://arxiv.org/abs/1708.05521v1
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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