论文标题
一种自适应深度聚类管道，以大规模告知文本标签
An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale
论文作者
论文摘要
从大量自然语言输入中挖掘潜在意图是帮助数据分析师设计并完善智能虚拟助手（IVA）的关键步骤，以提供客户服务和销售支持。我们在Verint Intent Manager（VIM）中创建了一个灵活且可扩展的聚类管道，该管道集成了语言模型的微调，高性能的K-NN库和社区检测技术，以帮助分析师快速浮出水面并从对话性文本中组织相关的用户意图。进行微调步骤是必要的，因为预训练的语言模型无法编码文本以有效地表面特定的聚类结构，而当目标文本来自看不见的域或聚类任务不是主题检测。我们描述了管道，并演示了其在三个现实世界文本挖掘任务上扩展的性能和能力。随着VIM应用程序中部署的方式，此聚类管道会产生高质量的结果，提高数据分析师的性能，并减少从客户服务数据中表达意图所需的时间，从而减少在新域中构建和部署IVA所需的时间。
Mining the latent intentions from large volumes of natural language inputs is a key step to help data analysts design and refine Intelligent Virtual Assistants (IVAs) for customer service and sales support. We created a flexible and scalable clustering pipeline within the Verint Intent Manager (VIM) that integrates the fine-tuning of language models, a high performing k-NN library and community detection techniques to help analysts quickly surface and organize relevant user intentions from conversational texts. The fine-tuning step is necessary because pre-trained language models cannot encode texts to efficiently surface particular clustering structures when the target texts are from an unseen domain or the clustering task is not topic detection. We describe the pipeline and demonstrate its performance and ability to scale on three real-world text mining tasks. As deployed in the VIM application, this clustering pipeline produces high quality results, improving the performance of data analysts and reducing the time it takes to surface intentions from customer service data, thereby reducing the time it takes to build and deploy IVAs in new domains.
