@inproceedings{dai-etal-2020-learning,
title = "Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment",
author = "Dai, Yinpei and
Li, Hangyu and
Tang, Chengguang and
Li, Yongbin and
Sun, Jian and
Zhu, Xiaodan",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.57",
doi = "10.18653/v1/2020.acl-main.57",
pages = "609--618",
abstract = "Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90{\%} per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.",
}
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<abstract>Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.</abstract>
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%0 Conference Proceedings
%T Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment
%A Dai, Yinpei
%A Li, Hangyu
%A Tang, Chengguang
%A Li, Yongbin
%A Sun, Jian
%A Zhu, Xiaodan
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F dai-etal-2020-learning
%X Existing end-to-end dialog systems perform less effectively when data is scarce. To obtain an acceptable success in real-life online services with only a handful of training examples, both fast adaptability and reliable performance are highly desirable for dialog systems. In this paper, we propose the Meta-Dialog System (MDS), which combines the advantages of both meta-learning approaches and human-machine collaboration. We evaluate our methods on a new extended-bAbI dataset and a transformed MultiWOZ dataset for low-resource goal-oriented dialog learning. Experimental results show that MDS significantly outperforms non-meta-learning baselines and can achieve more than 90% per-turn accuracies with only 10 dialogs on the extended-bAbI dataset.
%R 10.18653/v1/2020.acl-main.57
%U https://aclanthology.org/2020.acl-main.57
%U https://doi.org/10.18653/v1/2020.acl-main.57
%P 609-618
Markdown (Informal)
[Learning Low-Resource End-To-End Goal-Oriented Dialog for Fast and Reliable System Deployment](https://aclanthology.org/2020.acl-main.57) (Dai et al., ACL 2020)
ACL