Multi-label text classification is a critical task in the industry. It helps to extract structured information from large amount of textual data. We propose Text to Topic (Text2Topic), which achieves high multi-label classification performance by employing a Bi-Encoder Transformer architecture that utilizes concatenation, subtraction, and multiplication of embeddings on both text and topic. Text2Topic also supports zero-shot predictions, produces domain-specific text embeddings, and enables production-scale batch-inference with high throughput. The final model achieves accurate and comprehensive results compared to state-of-the-art baselines, including large language models (LLMs). In this study, a total of 239 topics are defined, and around 1.6 million text-topic pairs annotations (in which 200K are positive) are collected on approximately 120K texts from 3 main data sources on Booking.com. The data is collected with optimized smart sampling and partial labeling. The final Text2Topic model is deployed on a real-world stream processing platform, and it outperforms other models with 92.9% micro mAP, as well as a 75.8% macro mAP score. We summarize the modeling choices which are extensively tested through ablation studies, and share detailed in-production decision-making steps.

翻译：多标签文本分类是工业界的关键任务，有助于从海量文本数据中提取结构化信息。我们提出Text2Topic（文本到主题分类系统），该模型采用基于Bi-Encoder Transformer架构，通过文本与主题嵌入向量的拼接、相减与相乘操作，实现了高精度多标签分类。Text2Topic支持零样本预测、生成领域特定文本嵌入，并具备生产级高吞吐量批量推理能力。最终模型在包括大语言模型（LLMs）在内的先进基线方法中取得了准确且全面的结果。本研究共定义239个主题，从Booking.com三大主要数据源中约12万条文本收集了约160万个文本-主题对标注（其中20万条为正样本）。数据通过优化智能采样与部分标注策略收集。最终Text2Topic模型部署于真实流处理平台，以92.9%的微平均平均精确率（micro mAP）和75.8%的宏平均平均精确率（macro mAP）优于其他模型。我们总结了通过消融实验广泛验证的建模选择，并分享了详细的生产决策步骤。