Harmonized system code prediction of import and export commodities based on Hybrid Convolutional Neural Network with Auxiliary Network

Abstract

China Customs mainly uses manual inspections on the tax rates of import and export commodities, which can only cover a small part of the mass of commodities. Therefore, we investigate the natural language processing technology to determine the tax rate automatically by commodities classification. However, the unique challenge is that the structured commodity text is ambiguous, and has no continuous context and semantics, leading to difficulties for classification. In light of this challenge, we draw on the idea of the deep pyramid convolutional model and propose a Shallow Structured Convolutional Neural Network (SSCNN) with an Auxiliary Network to reduce the semantic fusion in commodity classification. When extracting shallow features, our model uses a structural token to fill in the feature boundary of structured text to prevent the feature fusion problem of adjacent features brought by the convolution operation. Auxiliary Network learns the distinguishing features of each commodity category and integrates the customs-specific knowledge to improve the classification performance of similar goods. In the empirical study on a real-world customs dataset, our model outperforms the mainstream deep learning methods including Transformer, BERT, BART and RoBERTa, which verifies the effectiveness of this method on classifying import and export commodities.