论文标题
带有潜在变量的序性主题选择模型，用于主题接地对话
Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue
论文作者
论文摘要
最近，由于其在预测下一个主题方面的有效性，通过历史上下文和主题顺序产生更好的响应，因此引起了主题接地的对话系统。但是，几乎所有现有的主题预测解决方案仅着眼于当前的对话和相应的主题顺序来预测下一个对话主题，而无需利用其他主题指导的对话，这些对话可能包含相关的主题转换对当前对话。为了解决这个问题，在本文中，我们提出了一种新颖的方法，称为顺序全球主题注意（SGTA），以微妙的方式利用所有对话，以更好地建模后响应主题转换，并指导当前对话的响应生成。具体而言，我们引入了一个带有混合内核功能的多元偏度正常分布的潜在空间，以灵活地将全局级别的信息与序列级别的信息集成在一起，并基于分布采样结果预测主题。我们还利用了一种主题意识的先验方法来进行预测主题的辅助选择，该方法可用于优化响应生成任务。广泛的实验表明，我们的模型在预测和发电任务上的表现优于竞争基准。
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.
