@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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<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.</abstract>
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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
Markdown (Informal)
[ConvLab: Multi-Domain End-to-End Dialog System Platform](https://aclanthology.org/P19-3011) (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.