@inproceedings{winata-etal-2019-caire,
title = "{CA}i{RE}{\_}{HKUST} at {S}em{E}val-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification",
author = "Winata, Genta Indra and
Madotto, Andrea and
Lin, Zhaojiang and
Shin, Jamin and
Xu, Yan and
Xu, Peng and
Fung, Pascale",
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/S19-2021",
doi = "10.18653/v1/S19-2021",
pages = "142--147",
abstract = "Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions. We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-to-end neural network models, and Gaussian processes for automatic hyper-parameter search. In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77{\%} F1-score on the test set.",
}
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<abstract>Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions. We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-to-end neural network models, and Gaussian processes for automatic hyper-parameter search. In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77% F1-score on the test set.</abstract>
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%0 Conference Proceedings
%T CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
%A Winata, Genta Indra
%A Madotto, Andrea
%A Lin, Zhaojiang
%A Shin, Jamin
%A Xu, Yan
%A Xu, Peng
%A Fung, Pascale
%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 winata-etal-2019-caire
%X Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, we introduce a hierarchical approach to classify emotion, hypothesizing that the current emotional state depends on previous latent emotions. We benchmark several feature-based classifiers using pre-trained word and emotion embeddings, state-of-the-art end-to-end neural network models, and Gaussian processes for automatic hyper-parameter search. In our experiments, hierarchical architectures consistently give significant improvements, and our best model achieves a 76.77% F1-score on the test set.
%R 10.18653/v1/S19-2021
%U https://aclanthology.org/S19-2021
%U https://doi.org/10.18653/v1/S19-2021
%P 142-147
Markdown (Informal)
[CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification](https://aclanthology.org/S19-2021) (Winata et al., SemEval 2019)
ACL
- Genta Indra Winata, Andrea Madotto, Zhaojiang Lin, Jamin Shin, Yan Xu, Peng Xu, and Pascale Fung. 2019. CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 142–147, Minneapolis, Minnesota, USA. Association for Computational Linguistics.