ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection

José-Ángel González, Lluís-F. Hurtado, Ferran Pla


Abstract
This paper describes the approach developed by the ELiRF-UPV team at SemEval 2019 Task 3: Contextual Emotion Detection in Text. We have developed a Snapshot Ensemble of 1D Hierarchical Convolutional Neural Networks to extract features from 3-turn conversations in order to perform contextual emotion detection in text. This Snapshot Ensemble is obtained by averaging the models selected by a Genetic Algorithm that optimizes the evaluation measure. The proposed ensemble obtains better results than a single model and it obtains competitive and promising results on Contextual Emotion Detection in Text.
Anthology ID:
S19-2031
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:
195–199
Language:
URL:
https://aclanthology.org/S19-2031
DOI:
10.18653/v1/S19-2031
Bibkey:
Cite (ACL):
José-Ángel González, Lluís-F. Hurtado, and Ferran Pla. 2019. ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 195–199, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
ELiRF-UPV at SemEval-2019 Task 3: Snapshot Ensemble of Hierarchical Convolutional Neural Networks for Contextual Emotion Detection (González et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/S19-2031.pdf