  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.
