@inproceedings{huang-etal-2019-ana,
title = "{ANA} at {S}em{E}val-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical {LSTM}s and {BERT}",
author = {Huang, Chenyang and
Trabelsi, Amine and
Za{\"\i}ane, Osmar},
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/S19-2006",
doi = "10.18653/v1/S19-2006",
pages = "49--53",
abstract = "This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchi- cal LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational con- text. The results show that, in this task, our HRCLE outperforms the most recent state-of- the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.",
}
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<abstract>This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchi- cal LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational con- text. The results show that, in this task, our HRCLE outperforms the most recent state-of- the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.</abstract>
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%0 Conference Proceedings
%T ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT
%A Huang, Chenyang
%A Trabelsi, Amine
%A Zaïane, Osmar
%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 huang-etal-2019-ana
%X This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchi- cal LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational con- text. The results show that, in this task, our HRCLE outperforms the most recent state-of- the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.
%R 10.18653/v1/S19-2006
%U https://aclanthology.org/S19-2006
%U https://doi.org/10.18653/v1/S19-2006
%P 49-53
Markdown (Informal)
[ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT](https://aclanthology.org/S19-2006) (Huang et al., SemEval 2019)
ACL