基于ABSA與動(dòng)態(tài)少樣本提示的主觀知識(shí)對(duì)話回復(fù)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
經(jīng)典的任務(wù)導(dǎo)向型對(duì)話系統(tǒng)主要依賴于事實(shí)性知識(shí)，例如文獻(xiàn)[1＼～4]中所使用的常見問題（FAQ）數(shù)據(jù)庫。（剩余18756字）