论文标题
柏拉图对话系统：灵活的对话人工智能研究平台
Plato Dialogue System: A Flexible Conversational AI Research Platform
论文作者
论文摘要
随着口语对话系统和对话AI的领域的增长，对工具和环境的需求也随之而来，这些工具和环境会抽象实施细节以加快开发过程，降低进入该领域的障碍，并为新想法提供常见的测试床。在本文中，我们介绍了柏拉图，这是一个用Python编写的灵活的对话AI平台，支持任何类型的对话式架构，从标准体系结构到具有共同训练的组件，单方或多方交互的体系结构，以及对任何对话代理组件的离线或在线培训。柏拉图被设计为易于理解和调试，对训练每个组件的基础学习框架不可知。
As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI platform written in Python that supports any kind of conversational agent architecture, from standard architectures to architectures with jointly-trained components, single- or multi-party interactions, and offline or online training of any conversational agent component. Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
