Recently, topic-grounded dialogue system has attracted significant attention due to its effectiveness in predicting the next topic to yield better responses via the historical context and given topic sequence. However, almost all existing topic prediction solutions focus on only the current conversation and corresponding topic sequence to predict the next conversation topic, without exploiting other topic-guided conversations which may contain relevant topic-transitions to current conversation. To address the problem, in this paper we propose a novel approach, named Sequential Global Topic Attention (SGTA) to exploit topic transition over all conversations in a subtle way for better modeling post-to-response topic-transition and guiding the response generation to the current conversation. Specifically, we introduce a latent space modeled as a Multivariate Skew-Normal distribution with hybrid kernel functions to flexibly integrate the global-level information with sequence-level information, and predict the topic based on the distribution sampling results. We also leverage a topic-aware prior-posterior approach for secondary selection of predicted topics, which is utilized to optimize the response generation task. Extensive experiments demonstrate that our model outperforms competitive baselines on prediction and generation tasks.
翻译:最近,基于专题的对话系统因其在预测下一个专题时的效力而吸引了人们的极大关注,因为其通过历史背景和特定主题顺序预测出更好的反应。然而,几乎所有现有专题预测解决方案都仅仅侧重于当前的对话和相应的专题序列,以预测下一个对话专题,而没有利用可能包含相关专题向当前对话过渡的其他专题指导对话。为了解决这一问题,我们在本文件中提出了一个新颖的方法,名为 " 分阶段全球专题关注 ",以利用所有对话的专题过渡周期,以一种微妙的方式,更好地模拟后应对后专题过渡,并指导当前对话的应对生成。具体地说,我们引入了以多变式 Skew-Normal 模式建成的潜在空间,以混合核心功能为模型,以灵活地将全球一级的信息与序列级信息整合,并根据分布抽样结果预测专题。我们还利用一个专题事先认知方法,作为预测专题的二次选择,用于优化应对生成任务。广泛的实验表明,我们的模型在预测和生成任务上超越竞争性基线。