标题： 一种半监督可扩展的统一框架用于电子商务查询分类 显示英文标题

标题： A Semi-supervised Scalable Unified Framework for E-commerce Query Classification

摘要： 查询分类，包括意图和类别预测等多个子任务，在电子商务应用中至关重要。 电子商务查询通常较短且缺乏上下文，标签之间的信息无法使用，导致建模的先验信息不足。 现有的大多数工业查询分类方法依赖于用户的后续点击行为来构建训练样本，导致出现马太恶循环。 此外，查询分类的子任务缺乏统一的框架，导致算法优化效率低下。 在本文中，我们提出了一种新颖的半监督可扩展统一框架（SSUF），包含多个增强模块以统一查询分类任务。 知识增强模块利用世界知识来增强查询表示，解决查询信息不足的问题。 标签增强模块利用标签语义和半监督信号来减少对后续标签的依赖。 结构增强模块基于复杂的标签关系增强标签表示。 每个模块高度可插拔，可以根据每个子任务需要添加或删除输入特征。 我们进行了广泛的离线和在线A/B实验，结果表明SSUF显著优于最先进的模型。 显示英文摘要

摘要： Query classification, including multiple subtasks such as intent and category prediction, is vital to e-commerce applications. E-commerce queries are usually short and lack context, and the information between labels cannot be used, resulting in insufficient prior information for modeling. Most existing industrial query classification methods rely on users' posterior click behavior to construct training samples, resulting in a Matthew vicious cycle. Furthermore, the subtasks of query classification lack a unified framework, leading to low efficiency for algorithm optimization. In this paper, we propose a novel Semi-supervised Scalable Unified Framework (SSUF), containing multiple enhanced modules to unify the query classification tasks. The knowledge-enhanced module uses world knowledge to enhance query representations and solve the problem of insufficient query information. The label-enhanced module uses label semantics and semi-supervised signals to reduce the dependence on posterior labels. The structure-enhanced module enhances the label representation based on the complex label relations. Each module is highly pluggable, and input features can be added or removed as needed according to each subtask. We conduct extensive offline and online A/B experiments, and the results show that SSUF significantly outperforms the state-of-the-art models.