Plato Dialogue System: A Flexible Conversational AI Research Platform
This provides a practical solution for researchers and developers in conversational AI, though it is incremental as it builds on existing platform concepts.
The paper tackles the need for efficient development tools in conversational AI by introducing Plato, a flexible Python platform that supports various agent architectures and training modes, resulting in a tool that lowers entry barriers and provides a common test-bed.
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.