论文标题
假期去哪里：迈向混合型对话框以澄清用户目标
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
论文作者
论文摘要
大多数对话系统都认为，在开始交互之前，用户已经确定了明确而具体的目标。例如，用户确定了预订航班的出发，目的地和旅行时间。但是，在许多情况下，受经验和知识的限制，用户可能知道他们需要什么，但仍然难以通过确定所有必要的插槽来确定明确而特定的目标。 在本文中，我们确定了这一挑战，并通过收集新的人类对人类的混合型对话框来向前迈出一步。它包含4个对话类型和5个域的5K对话框和168K话语。在每个会话中，代理商首先提供与用户目标相关的知识，以帮助弄清楚明确而特定的目标，然后帮助实现这些目标。 此外，我们提出了一个具有新颖基于迅速的持续学习机制的混合型对话模型。具体而言，该机制使该模型能够通过有效利用现有的对话情况来不断地增强其对任何特定类型的能力。
Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a flight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to figure out clear and specific goals by determining all the necessary slots. In this paper, we identify this challenge and make a step forward by collecting a new human-to-human mixed-type dialog corpus. It contains 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains. Within each session, an agent first provides user-goal-related knowledge to help figure out clear and specific goals, and then help achieve them. Furthermore, we propose a mixed-type dialog model with a novel Prompt-based continual learning mechanism. Specifically, the mechanism enables the model to continually strengthen its ability on any specific type by utilizing existing dialog corpora effectively.
