Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning

Shabnam Tafreshi, Mona Diab


Abstract
Detection and classification of emotion categories expressed by a sentence is a challenging task due to subjectivity of emotion. To date, most of the models are trained and evaluated on single genre and when used to predict emotion in different genre their performance drops by a large margin. To address the issue of robustness, we model the problem within a joint multi-task learning framework. We train this model with a multigenre emotion corpus to predict emotions across various genre. Each genre is represented as a separate task, we use soft parameter shared layers across the various tasks. our experimental results show that this model improves the results across the various genres, compared to a single genre training in the same neural net architecture.
Anthology ID:
C18-1246
Volume:
Proceedings of the 27th International Conference on Computational Linguistics
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Editors:
Emily M. Bender, Leon Derczynski, Pierre Isabelle
Venue:
COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2905–2913
Language:
URL:
https://aclanthology.org/C18-1246
DOI:
Bibkey:
Cite (ACL):
Shabnam Tafreshi and Mona Diab. 2018. Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2905–2913, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Emotion Detection and Classification in a Multigenre Corpus with Joint Multi-Task Deep Learning (Tafreshi & Diab, COLING 2018)
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PDF:
https://aclanthology.org/C18-1246.pdf