面向推荐的对话生成任务旨在通过人机对话交互获取用户偏好,以实现精准推荐。针对现有研究工作存在对话推荐类型单一和生成回复质量低的问题,本文提出一种基于统一预训练语言模型(Unified Language Model pre-training,UniLM)的目标驱...面向推荐的对话生成任务旨在通过人机对话交互获取用户偏好,以实现精准推荐。针对现有研究工作存在对话推荐类型单一和生成回复质量低的问题,本文提出一种基于统一预训练语言模型(Unified Language Model pre-training,UniLM)的目标驱动的推荐对话生成模型(Goal Driven Recommendation-oriented Dialog Generation model, GDRDG)。该模型包括文本表示模块、多头编码模块、解码模块以及一种特殊的注意力掩码机制。其中,文本表示模块通过UniLM对输入文本进行向量化表示,确保模型能捕获文本的深层次语义特征;多头编码模块利用多头自注意力机制捕捉全局上下文信息,提高生成回复的连贯性和相关性;解码模块生成当前轮对话目标及基于该目标的回复,确保回复符合上下文并将对话向预期目标引导;特殊的注意力掩码机制则通过控制解码过程中的信息流,确保模型仅关注当前轮次相关信息,以提高回复质量。实验结果表明,GDRDG模型在BLEU、Distinct、F1和Hit@1等指标上均优于现有方法,验证了模型的有效性和先进性。展开更多
This paper presents a multi thread dialog model using extended state transition network model combined with an object oriented event model to specify and manage user interface. The model provides multi thread dialo...This paper presents a multi thread dialog model using extended state transition network model combined with an object oriented event model to specify and manage user interface. The model provides multi thread dialogs and the concurrent executing of user interface and application procedures. The forms of class based concurrency possible in the model emphasize the human computer interaction and exploit the concurrency across objects and within an object. Dialog descriptions using extended state transition networks, the automatic extraction of concurrency, the facilities for performing synchronization and communicating, generalization and specification, and the smooth transition from dialog design to programming language implementation are also given in this paper.展开更多
文摘面向推荐的对话生成任务旨在通过人机对话交互获取用户偏好,以实现精准推荐。针对现有研究工作存在对话推荐类型单一和生成回复质量低的问题,本文提出一种基于统一预训练语言模型(Unified Language Model pre-training,UniLM)的目标驱动的推荐对话生成模型(Goal Driven Recommendation-oriented Dialog Generation model, GDRDG)。该模型包括文本表示模块、多头编码模块、解码模块以及一种特殊的注意力掩码机制。其中,文本表示模块通过UniLM对输入文本进行向量化表示,确保模型能捕获文本的深层次语义特征;多头编码模块利用多头自注意力机制捕捉全局上下文信息,提高生成回复的连贯性和相关性;解码模块生成当前轮对话目标及基于该目标的回复,确保回复符合上下文并将对话向预期目标引导;特殊的注意力掩码机制则通过控制解码过程中的信息流,确保模型仅关注当前轮次相关信息,以提高回复质量。实验结果表明,GDRDG模型在BLEU、Distinct、F1和Hit@1等指标上均优于现有方法,验证了模型的有效性和先进性。
文摘This paper presents a multi thread dialog model using extended state transition network model combined with an object oriented event model to specify and manage user interface. The model provides multi thread dialogs and the concurrent executing of user interface and application procedures. The forms of class based concurrency possible in the model emphasize the human computer interaction and exploit the concurrency across objects and within an object. Dialog descriptions using extended state transition networks, the automatic extraction of concurrency, the facilities for performing synchronization and communicating, generalization and specification, and the smooth transition from dialog design to programming language implementation are also given in this paper.
