SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations

Angelo Basile, Marc Franco-Salvador, Neha Pawar, Sanja Štajner, Mara Chinea Rios, Yassine Benajiba


Abstract
In this paper, we present our participation to the EmoContext shared task on detecting emotions in English textual conversations between a human and a chatbot. We propose four neural systems and combine them to further improve the results. We show that our neural ensemble systems can successfully distinguish three emotions (SAD, HAPPY, and ANGRY) and separate them from the rest (OTHERS) in a highly-imbalanced scenario. Our best system achieved a 0.77 F1-score and was ranked fourth out of 165 submissions.
Anthology ID:
S19-2057
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
330–334
Language:
URL:
https://aclanthology.org/S19-2057
DOI:
10.18653/v1/S19-2057
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/S19-2057.pdf
Data
EmoContext