Task-Oriented Clustering for Dialogues
Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, Xiaojie Wang
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.- Anthology ID:
- 2021.findings-emnlp.368
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4338–4347
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.368
- DOI:
- 10.18653/v1/2021.findings-emnlp.368
- Cite (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.
- Cite (Informal):
- Task-Oriented Clustering for Dialogues (Lv et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2021.findings-emnlp.368.pdf
- Code
- ryan-lv/dtcn
- Data
- SGD