Plato Research Dialogue System

The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.

The Plato Research Dialogue System is a flexible framework that can be used to create, train, and evaluate conversational AI agents in various environments. It supports interactions through speech, text, or dialogue acts and each conversational agent can interact with data, human users, or other conversational agents (in a multi-agent setting). Every component of every agent can be trained independently online or offline and Plato provides an easy way of wrapping around virtually any existing model, as long as Plato's interface is adhered to.

How does the Plato Research Dialogue System work?

Conceptually, a conversational agent needs to go through various steps in order to process information it receives as input (e.g., “What’s the weather like today?”) and produce an appropriate output (“Windy but not too cold.”). The primary steps, which correspond to the main components of a standard architecture, are:

Speech recognition (transcribe speech to text) Language understanding (extract meaning from that text) State tracking (aggregate information about what has been said and done so far) API call (search a database, query an API, etc.) Dialogue policy (generate abstract meaning of agent’s response) Language generation (convert abstract meaning into text) Speech synthesis (convert text into speech)

Plato has been designed to be as modular and flexible as possible; it supports traditional as well as custom conversational AI architectures, and importantly, enables multi-party interactions where multiple agents, potentially with different roles, can interact with each other, train concurrently, and solve distributed problems.