ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
- Chenyang Huang
- Amine Trabelsi
- Osmar R. Zaiane, University of Alberta (Database)
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-ofthe-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.
Citation
C. Huang, A. Trabelsi, O. Zaiane. "ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT". SemEval 2019 at the Annual Conference of the North American Chapter of the Association for Computati, Minneapolis, USA, pp 49-53, June 2019.| Keywords: | |
| Category: | In Conference |
| Web Links: | ACL |
| doi |
BibTeX
@incollection{Huang+al:NAACL-HLT19,
author = {Chenyang Huang and Amine Trabelsi and Osmar R. Zaiane},
title = {ANA at SemEval-2019 Task 3: Contextual Emotion detection in
Conversations through hierarchical LSTMs and BERT},
Pages = {49-53},
booktitle = {SemEval 2019 at the Annual Conference of the North American
Chapter of the Association for Computati},
year = 2019,
}Last Updated: September 15, 2020Submitted by Sabina P
