@inproceedings{chen-etal-2022-unidu,
title = "{U}ni{DU}: Towards A Unified Generative Dialogue Understanding Framework",
author = "Chen, Zhi and
Chen, Lu and
Chen, Bei and
Qin, Libo and
Liu, Yuncong and
Zhu, Su and
Lou, Jian-Guang and
Yu, Kai",
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-1.43",
doi = "10.18653/v1/2022.sigdial-1.43",
pages = "442--455",
abstract = "With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent models for each distinct DU task, without considering shared knowledge across different DU tasks. In this paper, we propose a unified generative dialogue understanding framework, named UniDU, to achieve effective information exchange across diverse DU tasks. Here, we reformulate all DU tasks into a unified prompt-based generative model paradigm. More importantly, a novel model-agnostic multi-task training strategy (MATS) is introduced to dynamically adapt the weights of diverse tasks for best knowlege sharing during training, based on the nature and available data of each task. Experiments on ten DU datasets covering five fundamental DU tasks show that the proposed UniDU framework largely outperforms task-specific well-designed methods on all tasks. MATS also reveals the knowledge sharing structure of these tasks. Finally, UniDU obtains promising performance on unseen dialogue domain, showing great potential of generalization.",
}
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<abstract>With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent models for each distinct DU task, without considering shared knowledge across different DU tasks. In this paper, we propose a unified generative dialogue understanding framework, named UniDU, to achieve effective information exchange across diverse DU tasks. Here, we reformulate all DU tasks into a unified prompt-based generative model paradigm. More importantly, a novel model-agnostic multi-task training strategy (MATS) is introduced to dynamically adapt the weights of diverse tasks for best knowlege sharing during training, based on the nature and available data of each task. Experiments on ten DU datasets covering five fundamental DU tasks show that the proposed UniDU framework largely outperforms task-specific well-designed methods on all tasks. MATS also reveals the knowledge sharing structure of these tasks. Finally, UniDU obtains promising performance on unseen dialogue domain, showing great potential of generalization.</abstract>
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%0 Conference Proceedings
%T UniDU: Towards A Unified Generative Dialogue Understanding Framework
%A Chen, Zhi
%A Chen, Lu
%A Chen, Bei
%A Qin, Libo
%A Liu, Yuncong
%A Zhu, Su
%A Lou, Jian-Guang
%A Yu, Kai
%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 chen-etal-2022-unidu
%X With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent models for each distinct DU task, without considering shared knowledge across different DU tasks. In this paper, we propose a unified generative dialogue understanding framework, named UniDU, to achieve effective information exchange across diverse DU tasks. Here, we reformulate all DU tasks into a unified prompt-based generative model paradigm. More importantly, a novel model-agnostic multi-task training strategy (MATS) is introduced to dynamically adapt the weights of diverse tasks for best knowlege sharing during training, based on the nature and available data of each task. Experiments on ten DU datasets covering five fundamental DU tasks show that the proposed UniDU framework largely outperforms task-specific well-designed methods on all tasks. MATS also reveals the knowledge sharing structure of these tasks. Finally, UniDU obtains promising performance on unseen dialogue domain, showing great potential of generalization.
%R 10.18653/v1/2022.sigdial-1.43
%U https://aclanthology.org/2022.sigdial-1.43
%U https://doi.org/10.18653/v1/2022.sigdial-1.43
%P 442-455
Markdown (Informal)
[UniDU: Towards A Unified Generative Dialogue Understanding Framework](https://aclanthology.org/2022.sigdial-1.43) (Chen et al., SIGDIAL 2022)
ACL
- Zhi Chen, Lu Chen, Bei Chen, Libo Qin, Yuncong Liu, Su Zhu, Jian-Guang Lou, and Kai Yu. 2022. UniDU: Towards A Unified Generative Dialogue Understanding Framework. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 442–455, Edinburgh, UK. Association for Computational Linguistics.