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.
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.
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.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.