@inproceedings{li-xing-2019-clp,
title = "{CLP} at {S}em{E}val-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection",
author = "Li, Changjie and
Xing, Yun",
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/S19-2025",
doi = "10.18653/v1/S19-2025",
pages = "164--168",
abstract = "In this paper, we describe the participation of team {''}CLP{''} in SemEval-2019 Task 3 {``}Con- textual Emotion Detection in Text{''} that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656.",
}
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<abstract>In this paper, we describe the participation of team ”CLP” in SemEval-2019 Task 3 “Con- textual Emotion Detection in Text” that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656.</abstract>
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%0 Conference Proceedings
%T CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection
%A Li, Changjie
%A Xing, Yun
%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 li-xing-2019-clp
%X In this paper, we describe the participation of team ”CLP” in SemEval-2019 Task 3 “Con- textual Emotion Detection in Text” that aims to classify emotion of user utterance in tex- tual conversation. The submitted system is a deep learning architecture based on Hier- archical Attention Networks (HAN) and Em- bedding from Language Model (ELMo). The core of the architecture contains two represen- tation layers. The first one combines the out- puts of ELMo, hand-craft features and Bidi- rectional Long Short-Term Memory with At- tention (Bi-LSTM-Attention) to represent user utterance. The second layer use a Bi-LSTM- Attention encoder to represent the conversa- tion. Our system achieved F1 score of 0.7524 which outperformed the baseline model of the organizers by 0.1656.
%R 10.18653/v1/S19-2025
%U https://aclanthology.org/S19-2025
%U https://doi.org/10.18653/v1/S19-2025
%P 164-168
Markdown (Informal)
[CLP at SemEval-2019 Task 3: Multi-Encoder in Hierarchical Attention Networks for Contextual Emotion Detection](https://aclanthology.org/S19-2025) (Li & Xing, SemEval 2019)
ACL