  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.
