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


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
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
64–69
URL:
https://www.aclweb.org/anthology/P19-3011
DOI:
10.18653/v1/P19-3011
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PDF:
https://www.aclweb.org/anthology/P19-3011.pdf