Attentive RNN for HS Code Hierarchy Classification on Vietnamese Goods Declaration

Huy Anh Nguyen, Nguyen Thanh Binh, Thang Ngoc Tran, Nguyen Linh Giang

Abstract: The harmonized commodity description and corresponding coding system (HS Code System) created by the World Customs Organization (WCO) are internationally used to classify standard transaction goods from their descriptions. The system uses the four-level hierarchical structure to arrange thousands of different codes. However, in practice, the traditional and manual methods for classifying a large number of items is a labor-consuming work and also prone to error. In order to assist the customs officers as well as many companies, we proposed a deep learning model with self-attention mechanism along side hierarchical classifying layers to improve the accuracy of classification of Vietnamese short text from goods declarations. Experimental results indicated the potential of these approaches with high accuracy.