@inproceedings{swamy-etal-2025-address,
    title = "An Address Intelligence Framework for {E}-commerce Deliveries",
    author = "Swamy, Gokul  and
      Gulati, Aman  and
      Virinchi, Srinivas  and
      Saladi, Anoop",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.70/",
    pages = "1026--1034",
    ISBN = "979-8-89176-333-3",
    abstract = "For an e-commerce domain, the customeraddress is the single most important pieceof customer data for ensuring accurateand reliable deliveries. In this two-partstudy, we first outline the construction ofa language model to assist customers withaddress standardization and in the latterpart, we detail a novel Pareto-ensemblemulti-task prediction algorithm that derives critical insights from customer addresses to minimize operational losses arising from a given geographical area. Finally, we demonstrate the potential benefits ofthe proposed address intelligence systemfor a large e-commerce domain throughlarge scale experiments on a commercialsystem."
}