ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

Chenyang Huang, Amine Trabelsi, Osmar Zaïane


Abstract
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchi- cal LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational con- text. The results show that, in this task, our HRCLE outperforms the most recent state-of- the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.
Anthology ID:
S19-2006
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
49–53
Language:
URL:
https://aclanthology.org/S19-2006
DOI:
10.18653/v1/S19-2006
Bibkey:
Cite (ACL):
Chenyang Huang, Amine Trabelsi, and Osmar Zaïane. 2019. ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 49–53, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT (Huang et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2006.pdf
Code
 chenyangh/SemEval2019Task3
Data
EmoContext