@inproceedings{ge-etal-2019-thu,
title = "{THU}{\_}{NGN} at {S}em{E}val-2019 Task 3: Dialog Emotion Classification using Attentional {LSTM}-{CNN}",
author = "Ge, Suyu and
Qi, Tao and
Wu, Chuhan and
Huang, Yongfeng",
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/S19-2059",
doi = "10.18653/v1/S19-2059",
pages = "340--344",
abstract = "With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users. It is important to understand the emotions through conversations to improve the quality of dialog systems. To facilitate the researches on dialog emotion recognition, the SemEval-2019 Task 3 named EmoContext is proposed. This task aims to classify the emotions of user utterance along with two short turns of dialogues into four categories. In this paper, we propose an attentional LSTM-CNN model to participate in this shared task. We use a combination of convolutional neural networks and long-short term neural networks to capture both local and long-distance contextual information in conversations. In addition, we apply attention mechanism to recognize and attend to important words within conversations. Besides, we propose to use ensemble strategies by combing the variants of our model with different pre-trained word embeddings via weighted voting. Our model achieved 0.7542 micro-F1 score in the final test data, ranking 15{\^{}}th out of 165 teams.",
}
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<abstract>With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users. It is important to understand the emotions through conversations to improve the quality of dialog systems. To facilitate the researches on dialog emotion recognition, the SemEval-2019 Task 3 named EmoContext is proposed. This task aims to classify the emotions of user utterance along with two short turns of dialogues into four categories. In this paper, we propose an attentional LSTM-CNN model to participate in this shared task. We use a combination of convolutional neural networks and long-short term neural networks to capture both local and long-distance contextual information in conversations. In addition, we apply attention mechanism to recognize and attend to important words within conversations. Besides, we propose to use ensemble strategies by combing the variants of our model with different pre-trained word embeddings via weighted voting. Our model achieved 0.7542 micro-F1 score in the final test data, ranking 15\^th out of 165 teams.</abstract>
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%0 Conference Proceedings
%T THU_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN
%A Ge, Suyu
%A Qi, Tao
%A Wu, Chuhan
%A Huang, Yongfeng
%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 ge-etal-2019-thu
%X With the development of the Internet, dialog systems are widely used in online platforms to provide personalized services for their users. It is important to understand the emotions through conversations to improve the quality of dialog systems. To facilitate the researches on dialog emotion recognition, the SemEval-2019 Task 3 named EmoContext is proposed. This task aims to classify the emotions of user utterance along with two short turns of dialogues into four categories. In this paper, we propose an attentional LSTM-CNN model to participate in this shared task. We use a combination of convolutional neural networks and long-short term neural networks to capture both local and long-distance contextual information in conversations. In addition, we apply attention mechanism to recognize and attend to important words within conversations. Besides, we propose to use ensemble strategies by combing the variants of our model with different pre-trained word embeddings via weighted voting. Our model achieved 0.7542 micro-F1 score in the final test data, ranking 15\^th out of 165 teams.
%R 10.18653/v1/S19-2059
%U https://aclanthology.org/S19-2059
%U https://doi.org/10.18653/v1/S19-2059
%P 340-344
Markdown (Informal)
[THU_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN](https://aclanthology.org/S19-2059) (Ge et al., SemEval 2019)
ACL