@inproceedings{agrawal-sembium-2025-rtsm,
    title = "{RTSM}: Knowledge Distillation with Diverse Signals for Efficient Real-Time Semantic Matching in {E}-Commerce",
    author = "Agrawal, Sanjay  and
      Sembium, Vivek",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.2/",
    pages = "9--19",
    ISBN = "979-8-89176-194-0",
    abstract = "Semantic matching plays a pivotal role in e-commerce by facilitating better product discovery and driving sales within online stores. Transformer models have proven exceptionally effective in mapping queries to an embedding space, positioning semantically related entities (queries or products) in close proximity. Despite their effectiveness, the high computational demands of large transformer models pose challenges for their deployment in real-time scenarios. This paper presents RTSM, an advanced knowledge distillation framework designed for Real-Time Semantic Matching. Our approach develops accurate, low-latency student models by leveraging both soft labels from a teacher model and ground truth generated from pairwise query-product and query-query signals. These signals are sourced from direct audits, synthetic examples created by LLMs, user interaction data, and taxonomy-based datasets, with custom loss functions enhancing learning efficiency. Experimental evaluations on internal and external e-commerce datasets demonstrate a 2-2.5{\%} increase in ROC-AUC compared to directly trained student models, outperforming both the teacher model and state-of-the-art knowledge distillation benchmarks."
}