Task-Oriented Clustering for Dialogues
Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, Xiaojie Wang · 2021
A reliable clustering algorithm for taskoriented 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 (DTCN) 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 outperformed strong baselines in all metrics 1 .