@inproceedings{lai-etal-2022-controllable,
title = "Controllable User Dialogue Act Augmentation for Dialogue State Tracking",
author = "Lai, Chun-Mao and
Hsu, Ming-Hao and
Huang, Chao-Wei and
Chen, Yun-Nung",
editor = "Lemon, Oliver and
Hakkani-Tur, Dilek and
Li, Junyi Jessy and
Ashrafzadeh, Arash and
Garcia, Daniel Hern{\'a}ndez and
Alikhani, Malihe and
Vandyke, David and
Du{\v{s}}ek, Ond{\v{r}}ej",
booktitle = "Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2022",
address = "Edinburgh, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sigdial-1.5",
doi = "10.18653/v1/2022.sigdial-1.5",
pages = "53--61",
abstract = "Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1.",
}
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<abstract>Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1.</abstract>
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%0 Conference Proceedings
%T Controllable User Dialogue Act Augmentation for Dialogue State Tracking
%A Lai, Chun-Mao
%A Hsu, Ming-Hao
%A Huang, Chao-Wei
%A Chen, Yun-Nung
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F lai-etal-2022-controllable
%X Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1.
%R 10.18653/v1/2022.sigdial-1.5
%U https://aclanthology.org/2022.sigdial-1.5
%U https://doi.org/10.18653/v1/2022.sigdial-1.5
%P 53-61
Markdown (Informal)
[Controllable User Dialogue Act Augmentation for Dialogue State Tracking](https://aclanthology.org/2022.sigdial-1.5) (Lai et al., SIGDIAL 2022)
ACL