Classification of Goods Using Text Descriptions With Sentences Retrieval
This work addresses a critical customs task for importers and exporters by reducing time and effort in tariff determination, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackles the problem of classifying traded goods into internationally accepted commodity codes (HS codes) using text descriptions, achieving 95.5% accuracy in top-3 suggestions for 265 subheadings on 129,084 past cases.
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.