Chenxu Lv


Task-Oriented Clustering for Dialogues
Chenxu Lv | Hengtong Lu | Shuyu Lei | Huixing Jiang | Wei Wu | Caixia Yuan | Xiaojie Wang
Findings of the Association for Computational Linguistics: EMNLP 2021

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.