@inproceedings{lee2019convlab,
author = {Lee, Sungjin and Zhu, Qi and Takanobu, Ryuichi and Li, Xiang and Zhang, Yaoqin and Zhang, Zheng and Li, Jinchao and Peng, Baolin and Li, Xiujun and Huang, Minlie and Gao, Jianfeng},
title = {ConvLab: Multi-Domain End-to-End Dialog System Platform},
booktitle = {ACL},
year = {2019},
month = {July},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/convlab-multi-domain-end-to-end-dialog-system-platform/},
}