@inproceedings{lee2019multi-domain,
author = {Lee, Sungjin and Schulz, Hannes and Atkinson, Adam and Gao, Jianfeng and Suleman, Kaheer and El Asri, Layla and Adada, Mahmoud and Huang, Minlie and Sharma, Shikhar and Tay, Wendy and Li, Xiujun},
title = {Multi-Domain Task-Completion Dialog Challenge},
booktitle = {Dialog System Technology Challenges 8},
year = {2019},
month = {March},
abstract = {This challenge intends to foster progress in two important aspects of dialog systems: dialog complexity and scaling to new domains. First, there is an increasing interest in building complex bots that span over multiple sub-domains to accomplish a complex user goal such as travel planning which may include hotel, restaurant, attraction and so on. To advance state-of-the-art technologies for handling complex dialogs, we over a timely task focusing on multi-domain end-to-end task completion dialog. Second, neural dialog systems require very large datasets to learn to output consistent and grammatically-correct sentences. This
makes it extremely hard to scale out the system to new domains with limited in-domain data. With this challenge, our goal is to investigate whether sample complexity can decrease with time, i.e., if a dialog system that was trained on a large corpus can learn to converse about a new domain given a much smaller in-domain corpus.},
url = {http://approjects.co.za/?big=en-us/research/publication/multi-domain-task-completion-dialog-challenge/},
}