中文版 | English
Title

Personalized Dialogue Generation with Persona-Adaptive Attention

Author
Corresponding AuthorZhang,Yu; Tang,H.
Publication Years
2023-06-27
Source Title
Volume
37
Pages
12916-12923
Abstract
Persona-based dialogue systems aim to generate consistent responses based on historical context and predefined persona. Unlike conventional dialogue generation, the persona-based dialogue needs to consider both dialogue context and persona, posing a challenge for coherent training. Specifically, this requires a delicate weight balance between context and persona. To achieve that, in this paper, we propose an effective framework with Persona-Adaptive Attention (PAA), which adaptively integrates the weights from the persona and context information via our designed attention. In addition, a dynamic masking mechanism is applied to the PAA to not only drop redundant information in context and persona but also serve as a regularization mechanism to avoid overfitting. Experimental results demonstrate the superiority of the proposed PAA framework compared to the strong baselines in both automatic and human evaluation. Moreover, the proposed PAA approach can perform equivalently well in a low-resource regime compared to models trained in a full-data setting, which achieve a similar result with only 20% to 30% of data compared to the larger models trained in the full-data setting. To fully exploit the effectiveness of our design, we designed several variants for handling the weighted information in different ways, showing the necessity and sufficiency of our weighting and masking designs.
SUSTech Authorship
Corresponding
Language
English
URL[Source Record]
Funding Project
National Natural Science Foundation of China[62076118];National Natural Science Foundation of China[62136005];Shenzhen Technical Project[JCYJ20210324105000003];
Scopus EID
2-s2.0-85167980364
Data Source
Scopus
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/559913
Affiliation
1.University of Surrey,United Kingdom
2.Southern University of Science and Technology,China
3.ByteDance AI Lab,China
4.MIT-IBM Watson AI Lab,United States
First Author AffilicationSouthern University of Science and Technology
Corresponding Author AffilicationSouthern University of Science and Technology
Recommended Citation
GB/T 7714
Huang,Qiushi,Zhang,Yu,Ko,Tom,et al. Personalized Dialogue Generation with Persona-Adaptive Attention[C],2023:12916-12923.
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