@inproceedings{chang-etal-2017-affordable,
title = "Affordable On-line Dialogue Policy Learning",
author = "Chang, Cheng and
Yang, Runzhe and
Chen, Lu and
Zhou, Xiang and
Yu, Kai",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1234",
doi = "10.18653/v1/D17-1234",
pages = "2200--2209",
abstract = "The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.",
}
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<abstract>The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.</abstract>
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%0 Conference Proceedings
%T Affordable On-line Dialogue Policy Learning
%A Chang, Cheng
%A Yang, Runzhe
%A Chen, Lu
%A Zhou, Xiang
%A Yu, Kai
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F chang-etal-2017-affordable
%X The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical. But in reality, due to the scarcity of real interaction data, the dialogue system usually grows slowly. Besides, the poor initial dialogue policy easily leads to bad user experience and incurs a failure of attracting users to contribute training data, so that the learning process is unsustainable. To accurately depict this, two quantitative metrics are proposed to assess safety and efficiency issues. For solving the unsustainable learning problem, we proposed a complete companion teaching framework incorporating the guidance from the human teacher. Since the human teaching is expensive, we compared various teaching schemes answering the question how and when to teach, to economically utilize teaching budget, so that make the online learning process affordable.
%R 10.18653/v1/D17-1234
%U https://aclanthology.org/D17-1234
%U https://doi.org/10.18653/v1/D17-1234
%P 2200-2209
Markdown (Informal)
[Affordable On-line Dialogue Policy Learning](https://aclanthology.org/D17-1234) (Chang et al., EMNLP 2017)
ACL
- Cheng Chang, Runzhe Yang, Lu Chen, Xiang Zhou, and Kai Yu. 2017. Affordable On-line Dialogue Policy Learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2200–2209, Copenhagen, Denmark. Association for Computational Linguistics.