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About

Plato Research Dialogue System

This is a v0.3.1 release.

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.

Publication citations:

Alexandros Papangelis, Yi-Chia Wang, Piero Molino, and Gokhan Tur, “Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning”, SIGDIAL 2019

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 (see Figure 1), 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.

Figures 1 and 2, below, depict example Plato conversational agent architectures when interacting with human users and with simulated users. Interacting with simulated users is a common practice used in the research community to jump-start learning (i.e., learn some basic behaviours before interacting with humans). Each individual component can be trained online or offline using any machine learning library (for instance, Ludwig, TensorFlow, PyTorch, your own implementations) as Plato is a universal framework. Ludwig, Uber's open source deep learning toolbox, makes for a good choice, as it does not require writing code and is fully compatible with Plato.

Figure 1: Plato's modular architecture means that any component can be trained online or offline and can be replaced by custom or pre-trained models. (Grayed components in this diagram are not core Plato components.)

Figure 2: Using a simulated user rather than a human user, as in Figure 1, we can pre-train statistical models for Plato's various components. These can then be used to create a prototype conversational agent that can interact with human users to collect more natural data that can be subsequently used to train better statistical models. (Grayed components in this diagram are not Plato core components.)

In addition to single-agent interactions, Plato supports multi-agent conversations where multiple Plato agents can interact with and learn from each other. Specifically, Plato will spawn the conversational agents, make sure that inputs and outputs (what each agent hears and says) are passed to each agent appropriately, and keep track of the conversation.

This setup can facilitate research in multi-agent learning, where agents need to learn how to generate language in order to perform a task, as well as research in sub-fields of multi-party interactions (dialogue state tracking, turn taking, etc.). The dialogue principles define what each agent can understand (an ontology of entities or meanings; for example: price, location, preferences, cuisine types, etc.) and what it can do (ask for more information, provide some information, call an API, etc.). The agents can communicate over speech, text, or structured information (dialogue acts) and each agent has its own configuration. Figure 3, below, depicts this architecture, outlining the communication between two agents and the various components:

Figure 3: Plato's architecture allows concurrent training of multiple agents, each with potentially different roles and objectives, and can facilitate research in fields such as multi-party interactions and multi-agent learning. (Grayed components in this diagram are not core Plato components.)

Finally, Plato supports custom architectures (e.g. splitting NLU into multiple independent components) and jointly-trained components (e.g. text-to-dialogue state, text-to-text, or any other combination) via the generic agent architecture shown in Figure 4, below. This mode moves away from the standard conversational agent architecture and supports any kind of architecture (e.g., with joint components, text-to-text or speech-to-speech components, or any other set-up) and allows loading existing or pre-trained models into Plato.

Figure 4: The most abstract version of a Plato agent (referred to as generic agent).

Users can define their own architecture and/or plug their own components into Plato by simply providing a Python class name and package path to that module, as well as the model’s initialization arguments. All the user needs do is list the modules in the order they should be executed and Plato takes care of the rest, including wrapping the input/output, chaining the modules, and handling the dialogues. Plato supports serial and parallel execution of modules.