Each utterance in multi-turn empathetic dialogues has features such as emotion, keywords, and utterance-level meaning. Feature transitions between utterances occur naturally. However, existing approaches fail to perceive the transitions because they extract features for the context at the coarse-grained level. To solve the above issue, we propose a novel approach of recognizing feature transitions between utterances, which helps understand the dialogue flow and better grasp the features of utterance that needs attention. Also, we introduce a response generation strategy to help focus on emotion and keywords related to appropriate features when generating responses. Experimental results show that our approach outperforms baselines and especially, achieves significant improvements on multi-turn dialogues.
|Title of host publication||NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics|
|Subtitle of host publication||Human Language Technologies, Proceedings of the Conference|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||11|
|Publication status||Published - 2022|
|Event||2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022 - Seattle, United States|
Duration: 2022 Jul 10 → 2022 Jul 15
|Name||NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference|
|Conference||2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022|
|Period||22/7/10 → 22/7/15|
Bibliographical noteFunding Information:
We thank all anonymous reviewers for their meaningful comments, and Hyeongjun Yang, Chan-hee Lee and Sunwoo Kang of Yonsei University for their discussion and feedback about our work. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP; Ministry of Science, ICT & Future Planning) (No. NRF-2022R1A2B5B01001835). Also, this work was partly supported by the Institute of Information and Communications Technology Planning and Evaluation(IITP) grant funded by the Korean government(MSIT) (No. 2020-0-01361-003, Artificial Intelligence Graduate School Program (Yonsei University)). Kyong-Ho Lee is the corresponding author.
© 2022 Association for Computational Linguistics.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Hardware and Architecture
- Information Systems