Query categorization at customer-to-customer e-commerce platforms like
Facebook Marketplace is challenging due to the vagueness of search intent,
noise in real-world data, and imbalanced training data across languages. Its
deployment also needs to consider challenges in scalability and downstream
integration in order to translate modeling advances into better search result
relevance. In this paper we present HierCat, the query categorization system at
Facebook Marketplace. HierCat addresses these challenges by leveraging
multi-task pre-training of dual-encoder architectures with a hierarchical
inference step to effectively learn from weakly supervised training data mined
from searcher engagement. We show that HierCat not only outperforms popular
methods in offline experiments, but also leads to 1.4% improvement in NDCG and
4.3% increase in searcher engagement at Facebook Marketplace Search in online
A/B testing.

本文提出了一种名为 HierCat 的查询分类系统，该系统通过使用双编码器体系结构的多任务预训练和分层推理步骤来从弱监督训练数据中有效地学习，以解决 Facebook Marketplace 等客户对客户电子商务平台上查询分类的挑战。实验结果表明，HierCat 不仅在离线实验中优于常用方法，而且在实际应用中，Facebook Marketplace Search 的 NDCG 指标有了 1.4% 的提升，并且搜索者的参与度增加了 4.3%。