@inproceedings{basile-etal-2019-symantoresearch,
title = "{S}ymanto{R}esearch at {S}em{E}val-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations",
author = "Basile, Angelo and
Franco-Salvador, Marc and
Pawar, Neha and
{\v{S}}tajner, Sanja and
Chinea Rios, Mara and
Benajiba, Yassine",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2057",
doi = "10.18653/v1/S19-2057",
pages = "330--334",
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.",
}
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%0 Conference Proceedings
%T SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations
%A Basile, Angelo
%A Franco-Salvador, Marc
%A Pawar, Neha
%A Štajner, Sanja
%A Chinea Rios, Mara
%A Benajiba, Yassine
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F basile-etal-2019-symantoresearch
%X 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.
%R 10.18653/v1/S19-2057
%U https://aclanthology.org/S19-2057
%U https://doi.org/10.18653/v1/S19-2057
%P 330-334
Markdown (Informal)
[SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations](https://aclanthology.org/S19-2057) (Basile et al., SemEval 2019)
ACL