# ConvLab: Multi-Domain End-to-End Dialog System Platform

arXiv: https://arxiv.org/abs/1904.08637 · HTML: https://mlsanity.com/paper/1904.08637/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 29 of 29)

- [ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL](https://mlsanity.com/paper/2402.19446/) (2024)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Transferable Dialogue Systems and User Simulators](https://mlsanity.com/paper/2107.11904/) (2021)
- [CRSLab: An Open-Source Toolkit for Building Conversational Recommender System](https://mlsanity.com/paper/2101.00939/) (2021)
- [Annotation Inconsistency and Entity Bias in MultiWOZ](https://mlsanity.com/paper/2105.14150/) (2021)
- [Interactive Grounded Language Understanding in a Collaborative Environment: IGLU 2021](https://mlsanity.com/paper/2205.02388/) (2022)
- [ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format](https://mlsanity.com/paper/2211.17148/) (2022)
- [OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts](https://mlsanity.com/paper/2109.12761/) (2021)
- [NeurIPS 2021 Competition IGLU: Interactive Grounded Language Understanding in a Collaborative Environment](https://mlsanity.com/paper/2110.06536/) (2021)
- [Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator](https://mlsanity.com/paper/2210.14529/) (2022)
- [Task-Oriented Dialogue System as Natural Language Generation](https://mlsanity.com/paper/2108.13679/) (2021)
- [DEUS: A Data-driven Approach to Estimate User Satisfaction in Multi-turn Dialogues](https://mlsanity.com/paper/2103.01287/) (2021)
- [Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management](https://mlsanity.com/paper/2104.04748/) (2021)
- [Enhancing End-to-End Multi-Task Dialogue Systems: A Study on Intrinsic Motivation Reinforcement Learning Algorithms for Improved Training and Adaptability](https://mlsanity.com/paper/2401.18040/) (2024)
- [OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue](https://mlsanity.com/paper/2209.04595/) (2022)
- [An Exploratory Study of AI System Risk Assessment from the Lens of Data Distribution and Uncertainty](https://mlsanity.com/paper/2212.06828/) (2022)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)
- [Unsupervised Extraction of Dialogue Policies from Conversations](https://mlsanity.com/paper/2406.15214/) (2024)
- [How to Evaluate Your Dialogue Models: A Review of Approaches](https://mlsanity.com/paper/2108.01369/) (2021)
- [A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems](https://mlsanity.com/paper/2205.05886/) (2022)
- [Dialog-to-Actions: Building Task-Oriented Dialogue System via Action-Level Generation](https://mlsanity.com/paper/2304.00884/) (2023)
- [DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models](https://mlsanity.com/paper/2401.02208/) (2024)
- [ShiJianBench: From Dialogue to Decision for Long-Horizon Evaluation of Investment Advisors](https://mlsanity.com/paper/2608.01204/) (2026)
- [Every time I fire a conversational designer, the performance of the dialog system goes down](https://mlsanity.com/paper/2109.13029/) (2021)
- [Active Learning and Multi-label Classification for Ellipsis and Coreference Detection in Conversational Question-Answering](https://mlsanity.com/paper/2207.03145/) (2022)
- [JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy Learning](https://mlsanity.com/paper/2309.00230/) (2023)
- [Dialog Action-Aware Transformer for Dialog Policy Learning](https://mlsanity.com/paper/2309.02240/) (2023)
- [Improving Dialogue Management: Quality Datasets vs Models](https://mlsanity.com/paper/2310.01339/) (2023)
- [Orcheo: A Modular Full-Stack Platform for Conversational Search](https://mlsanity.com/paper/2602.14710/) (2026)

## Abstract (arXiv)

We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments. ConvLab offers a set of fully annotated datasets and associated pre-trained reference models. As a showcase, we extend the MultiWOZ dataset with user dialog act annotations to train all component models and demonstrate how ConvLab makes it easy and effortless to conduct complicated experiments in multi-domain end-to-end dialog settings.