# Plato Dialogue System: A Flexible Conversational AI Research Platform

arXiv: https://arxiv.org/abs/2001.06463 · HTML: https://mlsanity.com/paper/2001.06463/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions](https://mlsanity.com/paper/2305.10435/) (2023)
- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [TextBox: A Unified, Modularized, and Extensible Framework for Text Generation](https://mlsanity.com/paper/2101.02046/) (2021)
- [Converse: A Tree-Based Modular Task-Oriented Dialogue System](https://mlsanity.com/paper/2203.12187/) (2022)
- [Using Large Language Models in Public Transit Systems, San Antonio as a case study](https://mlsanity.com/paper/2407.11003/) (2024)
- [How to Evaluate Your Dialogue Models: A Review of Approaches](https://mlsanity.com/paper/2108.01369/) (2021)
- [Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation](https://mlsanity.com/paper/2402.09954/) (2024)
- [WEBDial, a Multi-domain, Multitask Statistical Dialogue Framework with RDF](https://mlsanity.com/paper/2401.03905/) (2024)
- [Epistemoverse: Toward an AI-Driven Knowledge Metaverse for Intellectual Heritage Preservation](https://mlsanity.com/paper/2512.12201/) (2025)

## Abstract (arXiv)

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.