@inproceedings{liu-etal-2021-domain,
title = "Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks",
author = "Liu, Qingbin and
Cao, Pengfei and
Liu, Cao and
Chen, Jiansong and
Cai, Xunliang and
Yang, Fan and
He, Shizhu and
Liu, Kang and
Zhao, Jun",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.176/",
doi = "10.18653/v1/2021.emnlp-main.176",
pages = "2301--2311",
abstract = "Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems. Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance. This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains. Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. To this end, we propose a novel domain-lifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25{\%} and 8.27{\%} of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively."
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<abstract>Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems. Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance. This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains. Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. To this end, we propose a novel domain-lifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25% and 8.27% of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively.</abstract>
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%0 Conference Proceedings
%T Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks
%A Liu, Qingbin
%A Cao, Pengfei
%A Liu, Cao
%A Chen, Jiansong
%A Cai, Xunliang
%A Yang, Fan
%A He, Shizhu
%A Liu, Kang
%A Zhao, Jun
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liu-etal-2021-domain
%X Dialogue state tracking (DST), which estimates user goals given a dialogue context, is an essential component of task-oriented dialogue systems. Conventional DST models are usually trained offline, which requires a fixed dataset prepared in advance. This paradigm is often impractical in real-world applications since online dialogue systems usually involve continually emerging new data and domains. Therefore, this paper explores Domain-Lifelong Learning for Dialogue State Tracking (DLL-DST), which aims to continually train a DST model on new data to learn incessantly emerging new domains while avoiding catastrophically forgetting old learned domains. To this end, we propose a novel domain-lifelong learning method, called Knowledge Preservation Networks (KPN), which consists of multi-prototype enhanced retrospection and multi-strategy knowledge distillation, to solve the problems of expression diversity and combinatorial explosion in the DLL-DST task. Experimental results show that KPN effectively alleviates catastrophic forgetting and outperforms previous state-of-the-art lifelong learning methods by 4.25% and 8.27% of whole joint goal accuracy on the MultiWOZ benchmark and the SGD benchmark, respectively.
%R 10.18653/v1/2021.emnlp-main.176
%U https://aclanthology.org/2021.emnlp-main.176/
%U https://doi.org/10.18653/v1/2021.emnlp-main.176
%P 2301-2311
Markdown (Informal)
[Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks](https://aclanthology.org/2021.emnlp-main.176/) (Liu et al., EMNLP 2021)
ACL
- Qingbin Liu, Pengfei Cao, Cao Liu, Jiansong Chen, Xunliang Cai, Fan Yang, Shizhu He, Kang Liu, and Jun Zhao. 2021. Domain-Lifelong Learning for Dialogue State Tracking via Knowledge Preservation Networks. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2301–2311, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.