@inproceedings{lv-etal-2021-task-oriented,
title = "Task-Oriented Clustering for Dialogues",
author = "Lv, Chenxu and
Lu, Hengtong and
Lei, Shuyu and
Jiang, Huixing and
Wu, Wei and
Yuan, Caixia and
Wang, Xiaojie",
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.368",
doi = "10.18653/v1/2021.findings-emnlp.368",
pages = "4338--4347",
abstract = "A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression. In this paper, we propose a Dialogue Task Clustering Network model for task-oriented clustering. The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering. An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly. Experiments on three public datasets show that our model significantly outperform strong baselines in all metrics.",
}
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<abstract>A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression. In this paper, we propose a Dialogue Task Clustering Network model for task-oriented clustering. The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering. An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly. Experiments on three public datasets show that our model significantly outperform strong baselines in all metrics.</abstract>
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%0 Conference Proceedings
%T Task-Oriented Clustering for Dialogues
%A Lv, Chenxu
%A Lu, Hengtong
%A Lei, Shuyu
%A Jiang, Huixing
%A Wu, Wei
%A Yuan, Caixia
%A Wang, Xiaojie
%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 lv-etal-2021-task-oriented
%X A reliable clustering algorithm for task-oriented dialogues can help developer analysis and define dialogue tasks efficiently. It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression. In this paper, we propose a Dialogue Task Clustering Network model for task-oriented clustering. The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering. An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly. Experiments on three public datasets show that our model significantly outperform strong baselines in all metrics.
%R 10.18653/v1/2021.findings-emnlp.368
%U https://aclanthology.org/2021.findings-emnlp.368
%U https://doi.org/10.18653/v1/2021.findings-emnlp.368
%P 4338-4347
Markdown (Informal)
[Task-Oriented Clustering for Dialogues](https://aclanthology.org/2021.findings-emnlp.368) (Lv et al., Findings 2021)
ACL
- Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, and Xiaojie Wang. 2021. Task-Oriented Clustering for Dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4338–4347, Punta Cana, Dominican Republic. Association for Computational Linguistics.