An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade

Big Data and Cognitive Computing

James Roberto Bombasar¹  Valderi Reis Quietinho Leithardt²  Paul Crocker³  Bruno Alves da Silva⁴  Roberta Rodrigues de Lima⁴  Anita M. R. Fernandes⁴ 

[1] Analysis and Systems Development Course, Centro Universitário Avantis, Balneário Camboriú 88339-125, Brazil;COPELABS, Lusófona University of Humanities and Technologies, Campo Grande 376, 1749-024 Lisboa, Portugal;Instituto de Telecomunicações and Departamento de Informática, Universidade da Beira Interior, 6201-001 Covilhã, Portugal;Laboratory of Applied Intelligence, School of the Sea Science and Technology-University of Vale do Itajaí, Itajaí 88302-901, Brazil;

关键词: NCM classification;    natural language processing;    multilingual BERT;    Portuguese BERT;    transformers;    NLP;   

DOI  :  10.3390/bdcc6010008

来源: DOAJ

【 摘 要 】

Classification problems are common activities in many different domains and supervised learning algorithms have shown great promise in these areas. The classification of goods in international trade in Brazil represents a real challenge due to the complexity involved in assigning the correct category codes to a good, especially considering the tax penalties and legal implications of a misclassification. This work focuses on the training process of a classifier based on bidirectional encoder representations from transformers (BERT) for tax classification of goods with MCN codes which are the official classification system for import and export products in Brazil. In particular, this article presents results from using a specific Portuguese-language-pretrained BERT model, as well as results from using a multilingual-pretrained BERT model. Experimental results show that Portuguese model had a slightly better performance than the multilingual model, achieving an MCC 0.8491, and confirms that the classifiers could be used to improve specialists’ performance in the classification of goods.

【 授权许可】

Unknown