# User Modeling for Task Oriented Dialogues

arXiv: https://arxiv.org/abs/1811.04369 · HTML: https://mlsanity.com/paper/1811.04369/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

## Built on by (most cited first, 30 of 36)

- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [τ2-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://mlsanity.com/paper/2506.07982/) (2025)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Flipping the Dialogue: Training and Evaluating User Language Models](https://mlsanity.com/paper/2510.06552/) (2025)
- [Transferable Dialogue Systems and User Simulators](https://mlsanity.com/paper/2107.11904/) (2021)
- [User Simulation for Evaluating Information Access Systems](https://mlsanity.com/paper/2306.08550/) (2023)
- [Reliable LLM-based User Simulator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2402.13374/) (2024)
- [DuetSim: Building User Simulator with Dual Large Language Models for Task-Oriented Dialogues](https://mlsanity.com/paper/2405.13028/) (2024)
- [Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems](https://mlsanity.com/paper/2106.08838/) (2021)
- [GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers](https://mlsanity.com/paper/2208.10817/) (2022)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [Goal Alignment in LLM-Based User Simulators for Conversational AI](https://mlsanity.com/paper/2507.20152/) (2025)
- [In-Context Learning User Simulators for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2306.00774/) (2023)
- [Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems](https://mlsanity.com/paper/2104.09088/) (2021)
- [Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems](https://mlsanity.com/paper/2404.06762/) (2024)
- [Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play](https://mlsanity.com/paper/2210.12096/) (2022)
- [EmoUS: Simulating User Emotions in Task-Oriented Dialogues](https://mlsanity.com/paper/2306.01579/) (2023)
- [MatrAIx: Simulating the World with 8.3 Billion Persona Agents](https://mlsanity.com/paper/2608.04205/) (2026)
- [Teaching Models new APIs: Domain-Agnostic Simulators for Task Oriented Dialogue](https://mlsanity.com/paper/2110.06905/) (2021)
- [A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems](https://mlsanity.com/paper/2210.08692/) (2022)
- [Simulating User Agents for Embodied Conversational-AI](https://mlsanity.com/paper/2410.23535/) (2024)
- [Federated Natural Language Generation for Personalized Dialogue System](https://mlsanity.com/paper/2110.06419/) (2021)
- [How to Evaluate Your Dialogue Models: A Review of Approaches](https://mlsanity.com/paper/2108.01369/) (2021)
- [Adversarial learning of neural user simulators for dialogue policy optimisation](https://mlsanity.com/paper/2306.00858/) (2023)
- [Exploring Personality-Aware Interactions in Salesperson Dialogue Agents](https://mlsanity.com/paper/2504.18058/) (2025)
- [MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment](https://mlsanity.com/paper/2604.13828/) (2026)
- [BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systems](https://mlsanity.com/paper/2211.11982/) (2022)
- [It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation](https://mlsanity.com/paper/2310.00486/) (2023)
- [Simulating User Diversity in Task-Oriented Dialogue Systems using Large Language Models](https://mlsanity.com/paper/2502.12813/) (2025)
- [Program Synthesis Dialog Agents for Interactive Decision-Making](https://mlsanity.com/paper/2502.19610/) (2025)

## Abstract (arXiv)

We introduce end-to-end neural network based models for simulating users of task-oriented dialogue systems. User simulation in dialogue systems is crucial from two different perspectives: (i) automatic evaluation of different dialogue models, and (ii) training task-oriented dialogue systems. We design a hierarchical sequence-to-sequence model that first encodes the initial user goal and system turns into fixed length representations using Recurrent Neural Networks (RNN). It then encodes the dialogue history using another RNN layer. At each turn, user responses are decoded from the hidden representations of the dialogue level RNN. This hierarchical user simulator (HUS) approach allows the model to capture undiscovered parts of the user goal without the need of an explicit dialogue state tracking. We further develop several variants by utilizing a latent variable model to inject random variations into user responses to promote diversity in simulated user responses and a novel goal regularization mechanism to penalize divergence of user responses from the initial user goal. We evaluate the proposed models on movie ticket booking domain by systematically interacting each user simulator with various dialogue system policies trained with different objectives and users.