veinpy / ConvLab-2

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

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ConvLab-2

ConvLab-2 is an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems. As the successor of ConvLab, ConvLab-2 inherits ConvLab's framework but integrates more powerful dialogue models and supports more datasets. Besides, we have developed an analysis tool and an interactive tool to assist researchers in diagnosing dialogue systems. [paper]

Installation

Require python 3.6.

Clone this repository:

git clone https://github.com/thu-coai/ConvLab-2.git

Install ConvLab-2 via pip:

cd ConvLab-2
pip install -e .

Tutorials

Models

We provide following models:

  • NLU: SVMNLU, MILU, BERTNLU
  • DST: rule, MDBT, TRADE, SUMBT
  • Policy: rule, Imitation, REINFORCE, PPO, GDPL, MDRG, HDSA, LaRL
  • Simulator policy: Agenda, VHUS
  • NLG: Template, SCLSTM
  • End2End: Sequicity, DAMD, RNN_rollout

For more details about these models, You can refer to README.md under convlab2/$module/$model/$dataset dir such as convlab2/nlu/jointBERT/multiwoz/README.md.

Supported Datasets

  • Multiwoz 2.1
    • We add user dialogue act (inform, request, bye, greet, thank), remove 5 sessions that have incomplete dialogue act annotation and place it under data/multiwoz dir.
    • Train/val/test size: 8434/999/1000. Split as original data.
    • LICENSE: Attribution 4.0 International, url: http://creativecommons.org/licenses/by/4.0/
  • CrossWOZ
    • We offers a rule-based user simulator and a complete set of models for building a pipeline system on the CrossWOZ dataset. We correct few state annotation and place it under data/crosswoz dir.
    • Train/val/test size: 5012/500/500. Split as original data.
    • LICENSE: Attribution 4.0 International, url: http://creativecommons.org/licenses/by/4.0/
  • Camrest
    • We add system dialogue act (inform, request, nooffer) and place it under data/camrest dir.
    • Train/val/test size: 406/135/135. Split as original data.
    • LICENSE: Attribution 4.0 International, url: http://creativecommons.org/licenses/by/4.0/
  • Dealornot

End-to-end Performance on MultiWOZ

We perform end-to-end evaluation (1000 dialogues) on MultiWOZ using the user simulator below (a full example on tests/test_end2end.py) :

# BERT nlu trained on sys utterance
user_nlu = BERTNLU(mode='sys', config_file='multiwoz_sys_context.json', model_file='https://convlab.blob.core.windows.net/convlab-2/bert_multiwoz_sys_context.zip')
user_dst = None
user_policy = RulePolicy(character='usr')
user_nlg = TemplateNLG(is_user=True)
user_agent = PipelineAgent(user_nlu, user_dst, user_policy, user_nlg, name='user')

analyzer = Analyzer(user_agent=user_agent, dataset='multiwoz')

set_seed(20200202)
analyzer.comprehensive_analyze(sys_agent=sys_agent, model_name='sys_agent', total_dialog=1000)

Main metrics (refer to convlab2/evaluator/multiwoz_eval.py for more details):

  • Complete: whether complete the goal. Judged by the Agenda policy instead of external evaluator.
  • Success: whether all user requests have been informed and the booked entities satisfy the constraints.
  • Book: how many the booked entities satisfy the user constraints.
  • Inform Precision/Recall/F1: how many user requests have been informed.
  • Turn(succ/all): average turn number for successful/all dialogues.

Performance (the first row is the default config for each module. Empty entries are set to default config.):

NLU DST Policy NLG Complete rate Success rate Book rate Inform P/R/F1 Turn(succ/all)
BERTNLU RuleDST RulePolicy TemplateNLG 92.1 85.5 91.5 79.8/92.8/83.8 12.7/13.8
MILU RuleDST RulePolicy TemplateNLG 89.9 83.1 90.9 78.3/91.7/82.5 12.1/13.9
SVMNLU RuleDST RulePolicy TemplateNLG 84.2 70.4 76.1 79.1/88.8/81.5 14.8/17.7
BERTNLU RuleDST RulePolicy SCLSTM 40.1 41.0 51.5 68.5/56.5/59.1 11.6/29.2
BERTNLU RuleDST MLEPolicy TemplateNLG 52.6 48.4 35.5 66.3/72.7/66.0 12.5/26.3
BERTNLU RuleDST PGPolicy TemplateNLG 42.9 43.3 31.0 61.9/66.8/60.4 14.7/29.1
BERTNLU RuleDST PPOPolicy TemplateNLG 69.7 56.6 56.6 64.8/79.0/68.1 12.9/22.1
BERTNLU RuleDST GDPLPolicy TemplateNLG 57.9 49.5 33.5 67.0/76.4/68.2 11.5/24.3
None MDBT RulePolicy TemplateNLG 27.7 21.2 45.4 52.2/41.0/42.4 11.8/32.1
None TRADE RulePolicy TemplateNLG 29.9 25.3 36.9 49.3/48.1/44.4 12.7/24.7
None SUMBT RulePolicy TemplateNLG 34.7 33.8 57.8 52.3/50.6/47.3 12.1/26.6
BERTNLU RuleDST MDRG None 27.0 25.2 49.0 46.6/43.1/42.0 13.6/33.6
BERTNLU RuleDST HDSA None 35.6 27.5 5.4 47.8/57.2/48.8 13.0/31.5
BERTNLU RuleDST LaRL None 40.6 34.0 45.6 47.8/54.1/47.6 15.0/28.6
None SUMBT LaRL None 39.4 33.1 39.5 48.5/56.0/48.8 15.5/28.7
None None Sequicity* None 13.1 10.5 5.1 41.4/30.8/31.3 12.9/38.3
None None DAMD* None 38.5 33.6 50.9 62.1/60.7/57.4 10.4/28.2

*: end-to-end models used as sys_agent directly.

Module Performance on MultiWOZ

NLU

By running convlab2/nlu/evaluate.py MultiWOZ $model all:

Precision Recall F1
BERTNLU 82.48 85.59 84.01
MILU 80.29 83.63 81.92
SVMNLU 74.96 50.74 60.52

DST

By running convlab2/dst/evaluate.py MultiWOZ $model:

Joint accuracy Slot accuracy Joint F1
MDBT 0.06 0.89 0.43
SUMBT 0.30 0.96 0.83
TRADE 0.40 0.96 0.84

Policy

By running convlab2/policy/evalutate.py --model_name $model

Task Success Rate
MLE 0.56
PG 0.54
PPO 0.74
GDPL 0.58

NLG

By running convlab2/nlg/evaluate.py MultiWOZ $model sys

corpus BLEU-4
Template 0.3309
SCLSTM 0.4884

Issues

You are welcome to create an issue if you want to request a feature, report a bug or ask a general question.

Contributions

We welcome contributions from community.

  • If you want to make a big change, we recommend first creating an issue with your design.
  • Small contributions can be directly made by a pull request.
  • If you like make contributions to our library, see issues to find what we need.

Team

ConvLab-2 is maintained and developed by Tsinghua University Conversational AI group (THU-coai) and Microsoft Research (MSR).

We would like to thank:

Yan Fang, Zhuoer Feng, Jianfeng Gao, Qihan Guo, Kaili Huang, Minlie Huang, Sungjin Lee, Bing Li, Jinchao Li, Xiang Li, Xiujun Li, Wenchang Ma, Baolin Peng, Runze Liang, Ryuichi Takanobu, Jiaxin Wen, Yaoqin Zhang, Zheng Zhang, Qi Zhu, Xiaoyan Zhu.

Citing

If you use ConvLab-2 in your research, please cite:

@inproceedings{zhu2020convlab2,
    title={ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems},
    author={Qi Zhu and Zheng Zhang and Yan Fang and Xiang Li and Ryuichi Takanobu and Jinchao Li and Baolin Peng and Jianfeng Gao and Xiaoyan Zhu and Minlie Huang},
    year={2020},
    booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
}

License

Apache License 2.0

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ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems

License:Apache License 2.0


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