# Building a Conversational Agent Overnight with Dialogue Self-Play

arXiv: https://arxiv.org/abs/1801.04871 · HTML: https://mlsanity.com/paper/1801.04871/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2018.

## Built on by (most cited first, 30 of 70)

- [MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://mlsanity.com/paper/2305.14536/) (2023)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [KLUE: Korean Language Understanding Evaluation](https://mlsanity.com/paper/2105.09680/) (2021)
- [Dialogue State Tracking with a Language Model using Schema-Driven Prompting](https://mlsanity.com/paper/2109.07506/) (2021)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agents](https://mlsanity.com/paper/2305.13040/) (2023)
- [SIMMC 2.0: A Task-oriented Dialog Dataset for Immersive Multimodal Conversations](https://mlsanity.com/paper/2104.08667/) (2021)
- [BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling](https://mlsanity.com/paper/2106.02787/) (2021)
- [Zero-Shot Dialogue State Tracking via Cross-Task Transfer](https://mlsanity.com/paper/2109.04655/) (2021)
- [Controllable Dialogue Simulation with In-Context Learning](https://mlsanity.com/paper/2210.04185/) (2022)
- [Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk](https://mlsanity.com/paper/2401.05033/) (2024)
- [Zero-shot Generalization in Dialog State Tracking through Generative Question Answering](https://mlsanity.com/paper/2101.08333/) (2021)
- [Proactive Assistant Dialogue Generation from Streaming Egocentric Videos](https://mlsanity.com/paper/2506.05904/) (2025)
- [Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation](https://mlsanity.com/paper/2201.13405/) (2022)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://mlsanity.com/paper/2307.10172/) (2023)
- [End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs](https://mlsanity.com/paper/2109.07263/) (2021)
- [Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs](https://mlsanity.com/paper/2402.11633/) (2024)
- [MemGuide: Intent-Driven Memory Selection for Goal-Oriented Multi-Session LLM Agents](https://mlsanity.com/paper/2505.20231/) (2025)
- [NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation](https://mlsanity.com/paper/2105.14454/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems](https://mlsanity.com/paper/2104.09088/) (2021)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots](https://mlsanity.com/paper/2203.10759/) (2022)
- [BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets](https://mlsanity.com/paper/2210.12687/) (2022)
- [Interpretable Directed Diversity: Leveraging Model Explanations for Iterative Crowd Ideation](https://mlsanity.com/paper/2109.10149/) (2021)
- [HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent](https://mlsanity.com/paper/2402.01018/) (2024)
- [Where are we in semantic concept extraction for Spoken Language Understanding?](https://mlsanity.com/paper/2106.13045/) (2021)
- [Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play](https://mlsanity.com/paper/2210.12096/) (2022)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)

## Abstract (arXiv)

We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains. M2M scales to new tasks with just a task schema and an API client from the dialogue system developer, but it is also customizable to cater to task-specific interactions. Compared to the Wizard-of-Oz approach for data collection, M2M achieves greater diversity and coverage of salient dialogue flows while maintaining the naturalness of individual utterances. In the first phase, a simulated user bot and a domain-agnostic system bot converse to exhaustively generate dialogue "outlines", i.e. sequences of template utterances and their semantic parses. In the second phase, crowd workers provide contextual rewrites of the dialogues to make the utterances more natural while preserving their meaning. The entire process can finish within a few hours. We propose a new corpus of 3,000 dialogues spanning 2 domains collected with M2M, and present comparisons with popular dialogue datasets on the quality and diversity of the surface forms and dialogue flows.