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%。

HierCat: 在 Facebook Marketplace 上利用弱监督数据进行层级化查询分类

HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace

Task generalization has been a long standing challenge in Natural Language
Processing (NLP). Recent research attempts to improve the task generalization
ability of pre-trained language models by mapping NLP tasks into human-readable
prompted forms. However, these approaches require laborious and inflexible
manual collection of prompts, and different prompts on the same downstream task
may receive unstable performance. We propose Unified Schema Prompt, a flexible
and extensible prompting method, which automatically customizes the learnable
prompts for each task according to the task input schema. It models the shared
knowledge between tasks, while keeping the characteristics of different task
schema, and thus enhances task generalization ability. The schema prompt takes
the explicit data structure of each task to formulate prompts so that little
human effort is involved. To test the task generalization ability of schema
prompt at scale, we conduct schema prompt-based multitask pre-training on a
wide variety of general NLP tasks. The framework achieves strong zero-shot and
few-shot generalization performance on 16 unseen downstream tasks from 8 task
types (e.g., QA, NLI, etc). Furthermore, comprehensive analyses demonstrate the
effectiveness of each component in the schema prompt, its flexibility in task
compositionality, and its ability to improve performance under a full-data
fine-tuning setting.

该研究提出了一种名为 Unified Schema Prompt 的灵活可扩展的提示方法，通过利用每个任务的输入结构来自动自定义学习提示，以增强任务的泛化能力，并在一系列 NLP 任务中进行了实验验证。