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        <title>CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue</title>
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        <namePart type="family">Xu</namePart>
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    <name type="personal">
        <namePart type="given">Tiejun</namePart>
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        <namePart type="given">Sheng</namePart>
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            <title>Proceedings of the 19th Chinese National Conference on Computational Linguistics</title>
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        <name type="personal">
            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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            <namePart type="given">Yue</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>Emotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on social media. In this paper, we propose a hierarchical model to recognize emotions in the dialogue. In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN). Convolution is used to capture n-gram information and attention mechanism is used to obtain the relevant semantic information among words in the utterance. In the second layer, a GRU-based network helps to capture contextual information in the conversation. Furthermore, we discuss the effects of unidirectional and bidirectional networks. We conduct experiments on Friends dataset and EmotionPush dataset. The results show that our proposed model(CAN-GRU) and its variants achieve better performance than baselines.</abstract>
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%0 Conference Proceedings
%T CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue
%A Jiang, Ting
%A Xu, Bing
%A Zhao, Tiejun
%A Li, Sheng
%Y Sun, Maosong
%Y Li, Sujian
%Y Zhang, Yue
%Y Liu, Yang
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 October
%I Chinese Information Processing Society of China
%C Haikou, China
%G English
%F jiang-etal-2020-gru
%X Emotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on social media. In this paper, we propose a hierarchical model to recognize emotions in the dialogue. In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN). Convolution is used to capture n-gram information and attention mechanism is used to obtain the relevant semantic information among words in the utterance. In the second layer, a GRU-based network helps to capture contextual information in the conversation. Furthermore, we discuss the effects of unidirectional and bidirectional networks. We conduct experiments on Friends dataset and EmotionPush dataset. The results show that our proposed model(CAN-GRU) and its variants achieve better performance than baselines.
%U https://aclanthology.org/2020.ccl-1.102
%P 1101-1111

Markdown (Informal)

[CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue](https://aclanthology.org/2020.ccl-1.102) (Jiang et al., CCL 2020)

• CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue (Jiang et al., CCL 2020)

ACL

• Ting Jiang, Bing Xu, Tiejun Zhao, and Sheng Li. 2020. CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 1101–1111, Haikou, China. Chinese Information Processing Society of China.