Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136622
Title: Knowledge-enriched transformer for emotion detection in textual conversations
Authors: Zhong, Peixiang
Wang, Di
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Zhong, P., Wang, D., & Miao, C. (2019). Knowledge-enriched transformer for emotion detection in textual conversations. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 165-176. doi:10.18653/v1/D19-1016
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.
URI: https://hdl.handle.net/10356/136622
DOI: 10.18653/v1/D19-1016
Rights: © 1963-2019 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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