A Conditional Variational Framework for Dialog Generation
A Conditional Variational Framework for Dialog Generation
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DOI：
10.18653/v1/p17-2080
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Xiaoyu Shen;Hui Su;Yanran Li;Wenjie Li;Shuzi Niu;Yang Zhao;Akiko Aizawa;Guoping Long
中科院分区：
文献类型：
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作者：
Xiaoyu Shen;Hui Su;Yanran Li;Wenjie Li;Shuzi Niu;Yang Zhao;Akiko Aizawa;Guoping Long
深隐变量模型已被证明可以促进开放域对话系统的响应生成。然而，这些潜在变量是高度随机的，导致不可控的生成响应。在本文中，我们提出了一个允许基于特定属性生成条件响应的框架。可以手动分配这些属性，也可以自动检测这些属性。此外，两个说话人的对话状态分别建模，以反映个人特征。我们在两个不同的场景中验证了这个框架，其中属性分别引用了一般性和情感状态。实验结果证明了我们的模型的潜力，可以根据指定的属性生成有意义的响应。
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.