An effective emotion tendency perception model in empathic dialogue
Jiancu Chen,
Siyuan Yang,
Jiang Xiong and
Yiping Xiong
PLOS ONE, 2023, vol. 18, issue 3, 1-16
Abstract:
The effectiveness of open-domain dialogue systems depends heavily on emotion. In dialogue systems, previous models primarily detected emotions by looking for emotional words embedded in sentences. However, they did not precisely quantify the association of all words with emotions, which has led to a certain bias. To overcome this issue, we propose an emotion tendency perception model. The model uses an emotion encoder to accurately quantify the emotional tendencies of all words. Meanwhile, it uses a shared fusion decoder to equip the decoder with the sentiment and semantic capabilities of the encoder. We conducted extensive evaluations on Empathetic Dialogue. Experimental results demonstrate its efficacy. Compared with the state of the art, our approach has distinctive advantages.
Date: 2023
References: Add references at CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0282926 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 82926&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0282926
DOI: 10.1371/journal.pone.0282926
Access Statistics for this article
More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().