# End-to-End Task-Completion Neural Dialogue Systems

arXiv: https://arxiv.org/abs/1703.01008 · HTML: https://mlsanity.com/paper/1703.01008/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2017.

## Built on by (most cited first, 30 of 52)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents](https://mlsanity.com/paper/2410.14803/) (2024)
- [TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations](https://mlsanity.com/paper/2207.04154/) (2022)
- [Transferable Dialogue Systems and User Simulators](https://mlsanity.com/paper/2107.11904/) (2021)
- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues](https://mlsanity.com/paper/2109.00430/) (2021)
- [Diaformer: Automatic Diagnosis via Symptoms Sequence Generation](https://mlsanity.com/paper/2112.10433/) (2021)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Learning to Select External Knowledge with Multi-Scale Negative Sampling](https://mlsanity.com/paper/2102.02096/) (2021)
- [InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model](https://mlsanity.com/paper/2102.11008/) (2021)
- [Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations](https://mlsanity.com/paper/2401.04883/) (2024)
- [A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots](https://mlsanity.com/paper/2203.10759/) (2022)
- [Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning](https://mlsanity.com/paper/1811.07550/) (2018)
- [Multimodal Dialog Systems with Dual Knowledge-enhanced Generative Pretrained Language Model](https://mlsanity.com/paper/2207.07934/) (2022)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation](https://mlsanity.com/paper/2102.02191/) (2021)
- [UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://mlsanity.com/paper/2204.04637/) (2022)
- [Marrying Dialogue Systems with Data Visualization: Interactive Data Visualization Generation from Natural Language Conversations](https://mlsanity.com/paper/2307.16013/) (2023)
- [Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation](https://mlsanity.com/paper/2105.14445/) (2021)
- [Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System](https://mlsanity.com/paper/2207.11762/) (2022)
- [End-to-End Conversational Search for Online Shopping with Utterance Transfer](https://mlsanity.com/paper/2109.05460/) (2021)
- [The Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs](https://mlsanity.com/paper/2506.12266/) (2025)
- [Conversational Query Rewriting with Self-supervised Learning](https://mlsanity.com/paper/2102.04708/) (2021)
- [NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback](https://mlsanity.com/paper/2110.02148/) (2021)
- [Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning](https://mlsanity.com/paper/2207.12185/) (2022)
- [TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities](https://mlsanity.com/paper/2407.21693/) (2024)
- [clem:todd: A Framework for the Systematic Benchmarking of LLM-Based Task-Oriented Dialogue System Realisations](https://mlsanity.com/paper/2505.05445/) (2025)
- [High-Quality Diversification for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2106.00891/) (2021)
- [Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues](https://mlsanity.com/paper/2106.03065/) (2021)
- [SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching](https://mlsanity.com/paper/2110.11514/) (2021)

## Abstract (arXiv)

One of the major drawbacks of modularized task-completion dialogue systems is that each module is trained individually, which presents several challenges. For example, downstream modules are affected by earlier modules, and the performance of the entire system is not robust to the accumulated errors. This paper presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neural dialogue system can directly interact with a structured database to assist users in accessing information and accomplishing certain tasks. The reinforcement learning based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket booking domain show that our end-to-end system not only outperforms modularized dialogue system baselines for both objective and subjective evaluation, but also is robust to noises as demonstrated by several systematic experiments with different error granularity and rates specific to the language understanding module.