@inproceedings{lin-etal-2021-zero,
title = "Zero-Shot Dialogue State Tracking via Cross-Task Transfer",
author = "Lin, Zhaojiang and
Liu, Bing and
Madotto, Andrea and
Moon, Seungwhan and
Zhou, Zhenpeng and
Crook, Paul and
Wang, Zhiguang and
Yu, Zhou and
Cho, Eunjoon and
Subba, Rajen and
Fung, Pascale",
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.622",
doi = "10.18653/v1/2021.emnlp-main.622",
pages = "7890--7900",
abstract = "Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose to transfer the cross-task knowledge from general question answering (QA) corpora for the zero-shot DST task. Specifically, we propose TransferQA, a transferable generative QA model that seamlessly combines extractive QA and multi-choice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST. In addition, we introduce two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable our model to handle none value slots in the zero-shot DST setting. The extensive experiments show that our approaches substantially improve the existing zero-shot and few-shot results on MultiWoz. Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue dataset, our approach shows better generalization ability in unseen domains.",
}
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<abstract>Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose to transfer the cross-task knowledge from general question answering (QA) corpora for the zero-shot DST task. Specifically, we propose TransferQA, a transferable generative QA model that seamlessly combines extractive QA and multi-choice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST. In addition, we introduce two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable our model to handle none value slots in the zero-shot DST setting. The extensive experiments show that our approaches substantially improve the existing zero-shot and few-shot results on MultiWoz. Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue dataset, our approach shows better generalization ability in unseen domains.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Dialogue State Tracking via Cross-Task Transfer
%A Lin, Zhaojiang
%A Liu, Bing
%A Madotto, Andrea
%A Moon, Seungwhan
%A Zhou, Zhenpeng
%A Crook, Paul
%A Wang, Zhiguang
%A Yu, Zhou
%A Cho, Eunjoon
%A Subba, Rajen
%A Fung, Pascale
%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 lin-etal-2021-zero
%X Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data. In this work, we propose to transfer the cross-task knowledge from general question answering (QA) corpora for the zero-shot DST task. Specifically, we propose TransferQA, a transferable generative QA model that seamlessly combines extractive QA and multi-choice QA via a text-to-text transformer framework, and tracks both categorical slots and non-categorical slots in DST. In addition, we introduce two effective ways to construct unanswerable questions, namely, negative question sampling and context truncation, which enable our model to handle none value slots in the zero-shot DST setting. The extensive experiments show that our approaches substantially improve the existing zero-shot and few-shot results on MultiWoz. Moreover, compared to the fully trained baseline on the Schema-Guided Dialogue dataset, our approach shows better generalization ability in unseen domains.
%R 10.18653/v1/2021.emnlp-main.622
%U https://aclanthology.org/2021.emnlp-main.622
%U https://doi.org/10.18653/v1/2021.emnlp-main.622
%P 7890-7900
Markdown (Informal)
[Zero-Shot Dialogue State Tracking via Cross-Task Transfer](https://aclanthology.org/2021.emnlp-main.622) (Lin et al., EMNLP 2021)
ACL
- Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Zhenpeng Zhou, Paul Crook, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, and Pascale Fung. 2021. Zero-Shot Dialogue State Tracking via Cross-Task Transfer. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7890–7900, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.