Improving Address Matching using Siamese Transformer Networks
André V. Duarte Arlindo L. Oliveira
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.