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)
Copy Citation:
PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/P19-3011.pdf
Code
 budzianowski/multiwoz +  additional community code
Data
MultiWOZ