The Plato Research Dialogue System is a flexible, modular framework for building, training, and evaluating conversational AI agents. It supports text and speech interactions, multi-agent settings, and allows for independent training of components using various ML libraries.

How It Works

Plato agents process conversational turns through a pipeline of components like NLU, state tracking, dialogue policy, and NLG. Its core strength lies in its modularity, enabling users to swap in custom or pre-trained models for any component. It also facilitates multi-agent interactions and supports custom architectures beyond the standard pipeline, including jointly trained components.

Quick Start & Requirements

• Install from source: pip install -e . after cloning the repository.
• Prerequisites: Python 3.6+, TensorFlow (>=1.14.0), SpeechRecognition, PyAudio (with portaudio/gmp on macOS, python3-pyaudio on Ubuntu/Debian).
• Setup: Requires cloning the repo and installing dependencies.
• Docs: README

Highlighted Details

• Supports multi-agent training and interaction.
• Integrates with Ludwig for code-free deep learning model training.
• Offers rule-based components for quick prototyping and baselines.
• Includes parsers for datasets like DSTC2 and MetaLWOZ.

Maintenance & Community

• Developed by Uber AI.
• Key contributors listed in acknowledgements.
• No explicit community links (Discord/Slack) mentioned.

Licensing & Compatibility

• License: Not explicitly stated in the README.

Limitations & Caveats

• The project is at v0.3.1, indicating it may still be under active development with potential for breaking changes.
• Some use cases may not yet be supported, and many features are still being implemented.