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    ConvLab-3

    PyPI PyPI - Python Version GitHub

    ConvLab-3 is a flexible dialog system platform based on a unified data format for task-oriented dialog (TOD) datasets. The unified format serves as the adapter between TOD datasets and models: datasets are first transformed to the unified format and then loaded by models. In this way, the cost of adapting

    M
    models to
    N
    datasets is reduced from
    M\times N
    to
    M+N
    . While retaining all features of ConvLab-2, ConvLab-3 greatly enlarges supported datasets and models thanks to the unified format, and enhances the utility of reinforcement learning (RL) toolkit for dialog policy module. For typical usage, see our paper. Datasets and Trained models are also available on Hugging Face Hub.

    Updates

    • 2023.2.26: Update ConvLab on PyPI to 3.0.1 to reflect bug fixes.
    • 2022.11.30: ConvLab-3 release.

    Installation

    You can install ConvLab-3 in one of the following ways according to your need. We use torch>=1.10.1,<=1.13 and transformers>=4.17.0,<=4.24.0. Higher versions of torch and transformers may also work.

    Git clone and pip install in development mode (Recommend)

    For the latest and most configurable version, we recommend installing ConvLab-3 in development mode.

    Clone the newest repository:

    git clone --depth 1 https://github.com/ConvLab/ConvLab-3.git

    Install ConvLab-3 via pip:

    cd ConvLab-3
pip install -e .

    Pip install from PyPI

    To use ConvLab-3 as an off-the-shelf tool, you can install via:

    pip install convlab

    Note that the data directory will not be included due to the package size limitation.

    Using Docker

    We also provide Dockerfile for building docker. Basically it uses the requirement.txt and then installs ConvLab-3 in development mode.

    # create image
docker build -t convlab .

# run container
docker run -dit convlab

# open bash in container
docker exec -it CONTAINER_ID bash

    Tutorials

    Unified Datasets

    Current datasets in unified data format: (DA-U/DA-S stands for user/system dialog acts)

    Dataset Dialogs Goal DA-U DA-S State API result DataBase
    Camrest 676 ✅ ✅ ✅ ✅ ✅
    WOZ 2.0 1200 ✅ ✅
    KVRET 3030 ✅ ✅ ✅
    DailyDialog 13118 ✅
    Taskmaster-1 13175 ✅ ✅ ✅
    Taskmaster-2 17303 ✅ ✅ ✅
    MultiWOZ 2.1 10438 ✅ ✅ ✅ ✅ ✅
    Schema-Guided 22825 ✅ ✅ ✅ ✅
    MetaLWOZ 40203 ✅
    CrossWOZ (zh) 6012 ✅ ✅ ✅ ✅ ✅ ✅
    Taskmaster-3 23757 ✅ ✅ ✅ ✅

    Unified datasets are available under data/unified_datasets directory as well as Hugging Face Hub. We will continue adding more datasets listed in this issue. If you want to add a listed/custom dataset to ConvLab-3, you can create an issue for discussion and then create pull-request. We will list you as the contributors and highly appreciate your contributions!

    Models

    We list newly integrated models in ConvLab-3 that support unified data format and obtain strong performance. You can follow the link for more details about these models. Other models can be used in the same way as in ConvLab-2.

    Task Models Input Output
    Response Generation T5 Context Response
    Goal-to-Dialogue T5 Goal Dialog
    Natural Language Understanding T5, BERTNLU, MILU Context DA-U
    Dialog State Tracking T5, SUMBT, SetSUMBT, TripPy Context State
    RL Policy DDPT, PPO, PG State, DA-U, DB DA-S
    Word-Policy LAVA Context, State, DB Response
    Natural Language Generation T5, SC-GPT DA-S Response
    End-to-End SOLOIST Context, DB State, Response
    User simulator TUS, GenTUS Goal, DA-S DA-U, (Response)

    Trained models are available on Hugging Face Hub.

    Contributing

    We welcome contributions from community. Please see issues to find what we need.

    • If you want to add a new dataset, model, or other feature, please describe the dataset/model/feature in an issue with corresponding issue template before creating pull-request.
    • Small change like fixing a bug can be directly made by a pull-request.

    Code Structure

    .
├── convlab                             # Source code, installed in pypi package
│   ├── dialog_agent                    # Interface for dialog agent and session
│   ├── base_models
│   │   └── t5                          # T5 models with a unified training script
│   │       ├── goal2dialogue           # T5-Goal2Dialogue
│   │       ├── dst                     # T5-DST
│   │       ├── nlu                     # T5-NLU
│   │       ├── nlg                     # T5-NLG
│   │       └── rg                      # T5-RG
│   │
│   ├── nlu                             # NLU models, interface, and evaluation script
│   │   ├── jointBERT                   # BERTNLU
│   │   ├── milu                        # MILU
│   │   └── svm                         # SVMNLU*
│   │
│   ├── laug                            # Language understanding AUGmentation (LAUG) toolkit
│   │
│   ├── dst                             # DST models, interface, and evaluation script
│   │   ├── rule                        # RuleDST
│   │   ├── setsumbt                    # SetSUMBT, has uncertainty estimates
│   │   ├── sumbt                       # SUMBT
│   │   ├── trippy                      # TripPy
│   │   ├── trade                       # TRADE*
│   │   ├── comer                       # COMER*
│   │   ├── mdbt                        # MDBT*
│   │   └── dstc9                       # scripts for DSTC9 cross-lingual DST evaluation
│   │
│   ├── policy                          # Policy models, interface, and RL toolkit
│   │   ├── vector                      # vectorizer class
│   │   ├── plot_results                # RL plotting tool
│   │   ├── mle                         # MLE (imitation learning) policy
│   │   ├── pg                          # Policy Gradient
│   │   ├── ppo                         # Proximal Policy Optimization
│   │   ├── vtrace_DPT                  # DDPT
│   │   ├── lava                        # LAVA
│   │   ├── rule                        # Rule policies and rule-based user simulators 
│   │   ├── tus                         # TUS
│   │   ├── genTUS                      # GenTUS
│   │   ├── dqn                         # DQN*
│   │   ├── gdpl                        # GDPL*
│   │   ├── vhus                        # VHUS*
│   │   ├── hdsa                        # HDSA*
│   │   ├── larl                        # LARL*
│   │   └── mdrg                        # MDRG*
│   │
│   ├── nlg                             # NLG models, interface, and evaluation script
│   │   ├── scgpt                       # SC-GPT
│   │   ├── sclstm                      # SC-LSTM
│   │   └── template                    # TemplateNLG*
│   │
│   ├── e2e                             # End2End models
│   │   ├── soloist                     # SOLOIST
│   │   ├── damd                        # DAMD*
│   │   └── sequicity                   # Sequicity*
│   │
│   ├── evaluator                       # Evaluator for interactive evaluation
│   ├── human_eval                      # Human evaluation with AMT
│   ├── task                            # Goal generators for MultiWOZ, CrossWOZ, and Camrest
│   ├── util
│   │   └── unified_datasets_util.py    # Utility function for unified data format
│   └── deploy                          # Deploy system for human conversion
│
├── data                                # Data dir, not included in pypi package
│   ├── ...                             # ConvLab-2 data, not available for pypi installation
│   └── unified_datasets                # Unified datasets, available for pypi installation
├── examples
│   └── agent_examples                  # Examples of building user and system agents
└── tutorials                           # Tutorials

    *: models do not support unified datasets, only support MultiWOZ.

    Team

    ConvLab-3 is maintained and developed by Tsinghua University Conversational AI group (THU-COAI), the Dialogue Systems and Machine Learning Group at Heinrich Heine University, Düsseldorf, Germany and Microsoft Research (MSR).

    We would like to thank all contributors of ConvLab:

    Yan Fang, Zhuoer Feng, Jianfeng Gao, Qihan Guo, Kaili Huang, Minlie Huang, Sungjin Lee, Bing Li, Jinchao Li, Xiang Li, Xiujun Li, Jiexi Liu, Lingxiao Luo, Wenchang Ma, Mehrad Moradshahi, Baolin Peng, Runze Liang, Ryuichi Takanobu, Dazhen Wan, Hongru Wang, Jiaxin Wen, Yaoqin Zhang, Zheng Zhang, Qi Zhu, Xiaoyan Zhu, Carel van Niekerk, Christian Geishauser, Hsien-chin Lin, Nurul Lubis, Xiaochen Zhu, Michael Heck, Shutong Feng, Milica Gašić.

    Citing

    If you use ConvLab-3 in your research, please cite:

    @article{zhu2022convlab3,
    title={ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format},
    author={Qi Zhu and Christian Geishauser and Hsien-chin Lin and Carel van Niekerk and Baolin Peng and Zheng Zhang and Michael Heck and Nurul Lubis and Dazhen Wan and Xiaochen Zhu and Jianfeng Gao and Milica Gašić and Minlie Huang},
    journal={arXiv preprint arXiv:2211.17148},
    year={2022},
    url={http://arxiv.org/abs/2211.17148}
}

    License

    Apache License 2.0