CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM

Elham Mohammadi, Hessam Amini, Leila Kosseim

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Abstract
This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification. This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12%.
Anthology ID:
S19-2023
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:
153–158
Language:
URL:
https://aclanthology.org/S19-2023
DOI:
10.18653/v1/S19-2023
Bibkey:
Cite (ACL):
Elham Mohammadi, Hessam Amini, and Leila Kosseim. 2019. CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 153–158, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM (Mohammadi et al., SemEval 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/S19-2023.pdf
Data
EmoContext
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