@inproceedings{lee-2021-improving-end,
title = "Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task",
author = "Lee, Yohan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.112",
doi = "10.18653/v1/2021.findings-emnlp.112",
pages = "1296--1303",
abstract = "The paradigm of leveraging large pre-trained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-to-end TOD modeling by adopting span prediction as an auxiliary task. In end-to-end setting, our model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.",
}
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%0 Conference Proceedings
%T Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task
%A Lee, Yohan
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F lee-2021-improving-end
%X The paradigm of leveraging large pre-trained language models has made significant progress on benchmarks on task-oriented dialogue (TOD) systems. In this paper, we combine this paradigm with multi-task learning framework for end-to-end TOD modeling by adopting span prediction as an auxiliary task. In end-to-end setting, our model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 on MultiWOZ 2.0 and MultiWOZ 2.1, respectively. Furthermore, we demonstrate that multi-task learning improves not only the performance of model but its generalization capability through domain adaptation experiments in the few-shot setting. The code is available at github.com/bepoetree/MTTOD.
%R 10.18653/v1/2021.findings-emnlp.112
%U https://aclanthology.org/2021.findings-emnlp.112
%U https://doi.org/10.18653/v1/2021.findings-emnlp.112
%P 1296-1303
Markdown (Informal)
[Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task](https://aclanthology.org/2021.findings-emnlp.112) (Lee, Findings 2021)
ACL