Plato Dialogue System: A Flexible Conversational AI Research Platform

Abstract

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.

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Cited by in corpus (7)

• Survey on reinforcement learning for language processing
• ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
• Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems
• Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
• TextBox: A Unified, Modularized, and Extensible Framework for Text Generation
• Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
• How to Evaluate Your Dialogue Models: A Review of Approaches