Matching addresses is a critical task for companies and post offices involved in the processing and delivery of packages. The ramifications of incorrectly delivering a package to the wrong recipient are numerous, ranging from harm to the company's reputation to economic and environmental costs. This research introduces a deep learning-based model designed to increase the efficiency of address matching for Portuguese addresses. The model comprises two parts: (i) a bi-encoder, which is fine-tuned to create meaningful embeddings of Portuguese postal addresses, utilized to retrieve the top 10 likely matches of the un-normalized target address from a normalized database, and (ii) a cross-encoder, which is fine-tuned to accurately rerank the 10 addresses obtained by the bi-encoder. The model has been tested on a real-case scenario of Portuguese addresses and exhibits a high degree of accuracy, exceeding 95% at the door level. When utilized with GPU computations, the inference speed is about 4.5 times quicker than other traditional approaches such as BM25. An implementation of this system in a real-world scenario would substantially increase the effectiveness of the distribution process. Such an implementation is currently under investigation.

翻译：地址匹配是快递处理和投递过程中企业与邮政部门的核心任务。错误投递将给企业声誉、经济成本和环境资源带来多重负面影响。本研究提出了一种基于深度学习的葡萄牙语地址匹配优化模型。该模型由两部分组成：(i) 双编码器——通过微调生成葡萄牙邮政地址的有效嵌入向量，用于从未标准化目标地址的标准化数据库中检索前10个最匹配项；(ii) 交叉编码器——经过微调后对双编码器输出的10个候选地址进行精确重排序。模型在葡萄牙语地址真实场景中测试，在门牌号级别实现了超过95%的准确率。采用GPU计算时，推理速度较BM25等传统方法提升约4.5倍。将该系统投入实际应用将显著提高投递流程效率，相关部署方案正在研究中。