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

Sungjin Lee; Qi Zhu; Ryuichi Takanobu; Zheng Zhang; Yaoqin Zhang; Xiang Li (李翔); Jinchao Li; Baolin Peng; Xiujun Li; Minlie Huang; Jianfeng Gao

doi:10.18653/v1/P19-3011

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

Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao

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Abstract

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.

Anthology ID:: P19-3011
Volume:: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:: July
Year:: 2019
Address:: Florence, Italy
Editors:: Marta R. Costa-jussà, Enrique Alfonseca
Venue:: ACL
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 64–69
Language:
URL:: https://aclanthology.org/P19-3011/
DOI:: 10.18653/v1/P19-3011
Bibkey:
Cite (ACL):: Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, and Jianfeng Gao. 2019. ConvLab: Multi-Domain End-to-End Dialog System Platform. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 64–69, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):: ConvLab: Multi-Domain End-to-End Dialog System Platform (Lee et al., ACL 2019)
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PDF:: https://aclanthology.org/P19-3011.pdf

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@inproceedings{lee-etal-2019-convlab,
    title = "{C}onv{L}ab: Multi-Domain End-to-End Dialog System Platform",
    author = "Lee, Sungjin  and
      Zhu, Qi  and
      Takanobu, Ryuichi  and
      Zhang, Zheng  and
      Zhang, Yaoqin  and
      Li, Xiang  and
      Li, Jinchao  and
      Peng, Baolin  and
      Li, Xiujun  and
      Huang, Minlie  and
      Gao, Jianfeng",
    editor = "Costa-juss{\`a}, Marta R.  and
      Alfonseca, Enrique",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-3011/",
    doi = "10.18653/v1/P19-3011",
    pages = "64--69",
    abstract = "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."
}

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%0 Conference Proceedings
%T ConvLab: Multi-Domain End-to-End Dialog System Platform
%A Lee, Sungjin
%A Zhu, Qi
%A Takanobu, Ryuichi
%A Zhang, Zheng
%A Zhang, Yaoqin
%A Li, Xiang
%A Li, Jinchao
%A Peng, Baolin
%A Li, Xiujun
%A Huang, Minlie
%A Gao, Jianfeng
%Y Costa-jussà, Marta R.
%Y Alfonseca, Enrique
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F lee-etal-2019-convlab
%X 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.
%R 10.18653/v1/P19-3011
%U https://aclanthology.org/P19-3011/
%U https://doi.org/10.18653/v1/P19-3011
%P 64-69

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Markdown (Informal)

[ConvLab: Multi-Domain End-to-End Dialog System Platform](https://aclanthology.org/P19-3011/) (Lee et al., ACL 2019)

ConvLab: Multi-Domain End-to-End Dialog System Platform (Lee et al., ACL 2019)

ACL

Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, and Jianfeng Gao. 2019. ConvLab: Multi-Domain End-to-End Dialog System Platform. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 64–69, Florence, Italy. Association for Computational Linguistics.