Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection

• URL: http://arxiv.org/abs/2212.12363v1
• Date: Fri, 23 Dec 2022 14:36:43 GMT
• Title: Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection
• Authors: Zhitong Yang, Xing Ma, Anqi Liu, Zheyu Zhang
• Abstract summary: Task-oriented dialog aims to assist users in achieving specific goals through multi-turn conversation. We constructed a weakly supervised dataset based on a teacher/student paradigm. We also built a modular dialogue system and integrated coarse-to-fine grained classification for user intent detection.
• Score: 6.869753194843482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Task-oriented dialog(TOD) aims to assist users in achieving specific goals through multi-turn conversation. Recently, good results have been obtained based on large pre-trained models. However, the labeled-data scarcity hinders the efficient development of TOD systems at scale. In this work, we constructed a weakly supervised dataset based on a teacher/student paradigm that leverages a large collection of unlabelled dialogues. Furthermore, we built a modular dialogue system and integrated coarse-to-fine grained classification for user intent detection. Experiments show that our method can reach the dialog goal with a higher success rate and generate more coherent responses.

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