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            <namePart type="given">Kam-Fai</namePart>
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    <abstract>Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information. All present methods model historical utterances without considering the content of the target utterance. However, different parts of a historical utterance may contribute differently to emotion inference of different target utterances. Therefore we propose Fine-grained Extraction and Reasoning Network (FERNet) to generate target-specific historical utterance representations. The reasoning module effectively handles both local and global sequential dependencies to reason over context, and updates target utterance representations to more informed vectors. Experiments on two benchmarks show that our method achieves competitive performance compared with previous methods.</abstract>
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%0 Conference Proceedings
%T FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues
%A Guo, Yingmei
%A Wu, Zhiyong
%A Xu, Mingxing
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F guo-etal-2020-fernet
%X Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information. All present methods model historical utterances without considering the content of the target utterance. However, different parts of a historical utterance may contribute differently to emotion inference of different target utterances. Therefore we propose Fine-grained Extraction and Reasoning Network (FERNet) to generate target-specific historical utterance representations. The reasoning module effectively handles both local and global sequential dependencies to reason over context, and updates target utterance representations to more informed vectors. Experiments on two benchmarks show that our method achieves competitive performance compared with previous methods.
%R 10.18653/v1/2020.aacl-main.5
%U https://aclanthology.org/2020.aacl-main.5
%U https://doi.org/10.18653/v1/2020.aacl-main.5
%P 37-43

Markdown (Informal)

[FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues](https://aclanthology.org/2020.aacl-main.5) (Guo et al., AACL 2020)

• FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues (Guo et al., AACL 2020)

ACL

• Yingmei Guo, Zhiyong Wu, and Mingxing Xu. 2020. FERNet: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 37–43, Suzhou, China. Association for Computational Linguistics.