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

Edison Marrese-Taylor, Yutaka Matsuo


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.
Anthology ID:
W17-5232
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
233–237
Language:
URL:
https://aclanthology.org/W17-5232
DOI:
10.18653/v1/W17-5232
Bibkey:
Cite (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.
Cite (Informal):
EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity (Marrese-Taylor & Matsuo, WASSA 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-5232.pdf
Code
 epochx/emoatt