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.

本研究基于师生范式构建了一个弱监督数据集，整合粗细分类策略实现用户意图检测，实验表明，基于此方法的模块化对话系统能够以更高的成功率实现任务目标并生成更连贯的响应。

半监督学习与粗细意图检测探索客户服务对话系统

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

Existing text classification methods mainly focus on a fixed label set,
whereas many real-world applications require extending to new fine-grained
classes as the number of samples per label increases. To accommodate such
requirements, we introduce a new problem called coarse-to-fine grained
classification, which aims to perform fine-grained classification on coarsely
annotated data. Instead of asking for new fine-grained human annotations, we
opt to leverage label surface names as the only human guidance and weave in
rich pre-trained generative language models into the iterative weak supervision
strategy. Specifically, we first propose a label-conditioned finetuning
formulation to attune these generators for our task. Furthermore, we devise a
regularization objective based on the coarse-fine label constraints derived
from our problem setting, giving us even further improvements over the prior
formulation. Our framework uses the fine-tuned generative models to sample
pseudo-training data for training the classifier, and bootstraps on real
unlabeled data for model refinement. Extensive experiments and case studies on
two real-world datasets demonstrate superior performance over SOTA zero-shot
classification baselines.

该研究提出了一种基于粗到细粒度的分类，使用标签表面名称和预训练的生成式语言模型，生成虚假数据训练分类器并用于模型优化，实验证明在两个真实数据集上比零 - shot 分类改进了性能。