基于ABSA与动态少样本提示的主观知识对话回复生成模型

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

Rao Dongning，Zhuang Jietao （School ofComputers，Guangdong UniversityofTechnology，Guangzhou 51ooo6，China）

Abstract：Inthelatest task-oriented dialoguesystem challenges，efectivelyutilizing subjective knowledge（e.g.，personal opinions）iscrucialforaddresingusers’specificneeds.However，duetotheiherentlysubjectivenatureofsuchknowledge， howto efectively integrate and leveragethis information hasbecome a key focus of research.This paper proposeda method called DynSense，aimedataddresing thechallngeof generatingcomprehensiveand generalizedresponsesfrommultiplerelevant subjective user opinions.DynSense firstlyemployedaspect-basedsentiment analysis （ABSA）to parse the aspects and sentiment polarities withinsubjective knowledge snippets，aligning them with theuser’squery.Then，it utilizedanadvanced dialoguemodel thatcombined thedialoguecontext withABSA-enhanced information to generateresponses.AspeciallydesignedDynMatchalgorithm guidedthe model to generate morerelevantresponses bydynamicallselecting high-quality knowledgefragmentsmost similartothecurrentqueryasfew-shot prompts.The experimental resultsdemonstrate thatDynSense exhibits exceptionalabilityincapturing latentsemantic featuresand emotional tendencies，generating precise，comprehensive， andhighlyalignedresponses basedonpastuserreviews.Compared toexisting models，DynSenseshowssignificantimprovements across various evaluation metrics on the SK-TOD benchmark.

Key words：task-oriented dialogue systems；subjectiveknowledge；aspect-based sentiment analysis （ABSA）；dynamic fewshot prompts

0引言

经典的任务导向型对话系统主要依赖于事实性知识，例如文献[1＼～4]中所使用的常见问题（FAQ）数据库。（剩余18756字）