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Algorithm Research & Explore
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1706-1712

Subjective knowledge dialogue response generation model based on ABSA and dynamic few-shot prompting

Rao Dongning
Zhuang Jietao
School of Computers, Guangdong University of Technology, Guangzhou 510006, China

Abstract

In the latest task-oriented dialogue system challenges, effectively utilizing subjective knowledge(e. g., personal opinions) is crucial for addressing users' specific needs. However, due to the inherently subjective nature of such knowledge, how to effectively integrate and leverage this information has become a key focus of research. This paper proposed a method called DynSense, aimed at addressing the challenge of generating comprehensive and generalized responses from multiple relevant subjective user opinions. DynSense firstly employed aspect-based sentiment analysis(ABSA) to parse the aspects and sentiment polarities within subjective knowledge snippets, aligning them with the user's query. Then, it utilized an advanced dialogue model that combined the dialogue context with ABSA-enhanced information to generate responses. A specially designed DynMatch algorithm guided the model to generate more relevant responses by dynamically selecting high-quality know-ledge fragments most similar to the current query as few-shot prompts. The experimental results demonstrate that DynSense exhibits exceptional ability in capturing latent semantic features and emotional tendencies, generating precise, comprehensive, and highly aligned responses based on past user reviews. Compared to existing models, DynSense shows significant improvements across various evaluation metrics on the SK-TOD benchmark.

Foundation Support

广东省自然科学基金面上项目(2021A1515012556)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0468
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 6
Section: Algorithm Research & Explore
Pages: 1706-1712
Serial Number: 1001-3695(2025)06-014-1706-07

Publish History

[2025-03-10] Accepted Paper
[2025-06-05] Printed Article

Cite This Article

饶东宁, 庄杰涛. 基于ABSA与动态少样本提示的主观知识对话回复生成模型 [J]. 计算机应用研究, 2025, 42 (6): 1706-1712. (Rao Dongning, Zhuang Jietao. Subjective knowledge dialogue response generation model based on ABSA and dynamic few-shot prompting [J]. Application Research of Computers, 2025, 42 (6): 1706-1712. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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