论文标题
通过半监督学习和粗到精细的意图检测发现客户服务对话系统
Discovering Customer-Service Dialog System with Semi-Supervised Learning and Coarse-to-Fine Intent Detection
论文作者
论文摘要
以任务为导向的对话框（TOD）旨在通过多转交谈来帮助用户实现特定目标。最近，基于大型预训练模型获得了良好的结果。但是，标记的数据稀缺性会阻碍TOD系统的有效发展。在这项工作中，我们根据教师/学生范式构建了一个弱监督的数据集，该数据集利用了大量未标记的对话。此外，我们构建了一个模块化对话系统，并集成了粗到1的粒度分类，以进行用户意图检测。实验表明，我们的方法可以以更高的成功率达到对话目标，并产生更连贯的响应。
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.
