  The task of assigning and validating internationally accepted commodity code
(HS code) to traded goods is one of the critical functions at the customs
office. This decision is crucial to importers and exporters, as it determines
the tariff rate. However, similar to court decisions made by judges, the task
can be non-trivial even for experienced customs officers. The current paper
proposes a deep learning model to assist this seemingly challenging HS code
classification. Together with Korea Customs Service, we built a decision model
based on KoELECTRA that suggests the most likely heading and subheadings (i.e.,
the first four and six digits) of the HS code. Evaluation on 129,084 past cases
shows that the top-3 suggestions made by our model have an accuracy of 95.5% in
classifying 265 subheadings. This promising result implies algorithms may
reduce the time and effort taken by customs officers substantially by assisting
the HS code classification task.
