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        <title>Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations</title>
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        <namePart type="given">Peixiang</namePart>
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    <name type="personal">
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            <title>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</title>
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    <abstract>Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.</abstract>
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%0 Conference Proceedings
%T Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
%A Zhong, Peixiang
%A Wang, Di
%A Miao, Chunyan
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhong-etal-2019-knowledge
%X Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.
%R 10.18653/v1/D19-1016
%U https://aclanthology.org/D19-1016
%U https://doi.org/10.18653/v1/D19-1016
%P 165-176

Markdown (Informal)

[Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations](https://aclanthology.org/D19-1016) (Zhong et al., EMNLP-IJCNLP 2019)

• Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (Zhong et al., EMNLP-IJCNLP 2019)

ACL

• Peixiang Zhong, Di Wang, and Chunyan Miao. 2019. Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 165–176, Hong Kong, China. Association for Computational Linguistics.