Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 20 November 2021 / Approved: 22 November 2021 / Online: 22 November 2021 (10:59:43 CET)

A peer-reviewed article of this Preprint also exists.

de Lima, R.R.; Fernandes, A.M.R.; Bombasar, J.R.; da Silva, B.A.; Crocker, P.; Leithardt, V.R.Q. An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade. Big Data Cogn. Comput. 2022, 6, 8. de Lima, R.R.; Fernandes, A.M.R.; Bombasar, J.R.; da Silva, B.A.; Crocker, P.; Leithardt, V.R.Q. An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade. Big Data Cogn. Comput. 2022, 6, 8.

Journal reference: Big Data Cogn. Comput. 2022, 6, 8
DOI: 10.3390/bdcc6010008

Abstract

The classification of goods involved in international trade in Brazil is based on the Mercosur Common Nomenclature (NCM). The classification of these goods represents a real challenge due to the complexity involved in assigning the correct category codes especially considering the legal and fiscal implications of misclassification. This work focuses on the training of a classifier based on Bidirectional En-coder Representations from Transformers (BERT) for the tax classification of goods with NCM codes. In particular, this article presents results from using a specific Portuguese Language tuned BERT model as well results from using a Multilingual BERT. Experimental results justify the use of these models in the classification process and also that the language specific model has a slightly better performance.

Keywords

NCM classification; natural language processing; transformers; multilingual BERT; portuguese BERT; NLP; BERT

Subject

ENGINEERING, Other

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