利用对话模型引导的对话生成推荐
TP20; 对话推荐技术旨在通过与用户的对话交互完成高质量的信息推荐.针对已有研究存在的对话目标预测准确性不高的问题,提出一种利用对话模型引导的对话生成推荐(dialogue guided recommendation of dialogue generation,DGRDG)模型.首先,利用对话模型生成对话目标,通过经典的Seq2Seq模型融合输入的对话历史、用户画像以及知识信息来生成对话目标;其次,提出目标重规划策略(goal replan policy,GRP)来修正生成的对话目标,以提高对话目标预测的准确率.在DuRecDial数据集上进行实验的结果表明,对话目标生成模块在引入目标重规...
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| Published in | 东北大学学报（自然科学版） Vol. 43; no. 10; pp. 1397 - 1404 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
东北大学 计算机科学与工程学院, 辽宁 沈阳 110169
01.10.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1005-3026 |
| DOI | 10.12068/j.issn.1005-3026.2022.10.005 |
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| Abstract | TP20; 对话推荐技术旨在通过与用户的对话交互完成高质量的信息推荐.针对已有研究存在的对话目标预测准确性不高的问题,提出一种利用对话模型引导的对话生成推荐(dialogue guided recommendation of dialogue generation,DGRDG)模型.首先,利用对话模型生成对话目标,通过经典的Seq2Seq模型融合输入的对话历史、用户画像以及知识信息来生成对话目标;其次,提出目标重规划策略(goal replan policy,GRP)来修正生成的对话目标,以提高对话目标预测的准确率.在DuRecDial数据集上进行实验的结果表明,对话目标生成模块在引入目标重规划策略后,对话目标预测的准确率提高了3.93％;总体模型在BLEU,DISTINCT,F1以及人工评价指标上具有较好的效果. |
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| AbstractList | TP20; 对话推荐技术旨在通过与用户的对话交互完成高质量的信息推荐.针对已有研究存在的对话目标预测准确性不高的问题,提出一种利用对话模型引导的对话生成推荐(dialogue guided recommendation of dialogue generation,DGRDG)模型.首先,利用对话模型生成对话目标,通过经典的Seq2Seq模型融合输入的对话历史、用户画像以及知识信息来生成对话目标;其次,提出目标重规划策略(goal replan policy,GRP)来修正生成的对话目标,以提高对话目标预测的准确率.在DuRecDial数据集上进行实验的结果表明,对话目标生成模块在引入目标重规划策略后,对话目标预测的准确率提高了3.93％;总体模型在BLEU,DISTINCT,F1以及人工评价指标上具有较好的效果. |
| Author | 韩东红 高翟 乔百友 齐孝龙 |
| AuthorAffiliation | 东北大学 计算机科学与工程学院, 辽宁 沈阳 110169 |
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| Author_FL | GAO Di HAN Dong-hong QI Xiao-long QIAO Bai-you |
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