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
- 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)
- PDF:
- https://preview.aclanthology.org/naacl24-info/S19-2006.pdf
- Code
- chenyangh/SemEval2019Task3
- Data
- EmoContext