论文标题
喜欢远足吗？您可能享受自然：具有常识性扩展的角色接地对话
Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions
论文作者
论文摘要
现有的角色接地对话模型通常无法捕获给定角色描述的简单含义，这是人类能够无缝进行的。例如，最新的模型无法推断出对远足的兴趣可能意味着对自然或渴望休息。在本文中，我们建议使用现有常识性知识库和解释资源来扩展可用的角色句子，以使对话模型具有访问扩展和更丰富的角色描述。此外，我们通过鼓励模型在角色句子中做出离散选择，同时综合对话框响应，从而在角色上介绍细粒度的基础。由于数据中未观察到这种选择，因此我们使用离散的潜在随机变量对其进行建模，并使用变分学习从数百个角色扩展中进行采样。在对话质量和多样性方面，我们的模型在人行数据集上的表现优于竞争基线，同时实现了情绪上的一致和可控的对话框。
Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly. For example, state-of-the-art models cannot infer that interest in hiking might imply love for nature or longing for a break. In this paper, we propose to expand available persona sentences using existing commonsense knowledge bases and paraphrasing resources to imbue dialog models with access to an expanded and richer set of persona descriptions. Additionally, we introduce fine-grained grounding on personas by encouraging the model to make a discrete choice among persona sentences while synthesizing a dialog response. Since such a choice is not observed in the data, we model it using a discrete latent random variable and use variational learning to sample from hundreds of persona expansions. Our model outperforms competitive baselines on the PersonaChat dataset in terms of dialog quality and diversity while achieving persona-consistent and controllable dialog generation.
