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arXiv · 2212.12363

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

Zhitong Yang·Xing Ma·Anqi Liu·Zheyu Zhang
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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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Zhitong Yang, Xing Ma, Anqi Liu, Zheyu Zhang. 2022-12-23. Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection. https://arxiv.org/abs/2212.12363

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