Three-Layer Denoiser: Denoising Parallel Corpora for NMT Systems

  • Felipe de A. Florencio SiDi
  • Matheus S. de Lacerda SiDi
  • Anderson P. Cavalcanti SiDi
  • Vitor Rolim UFPE


In recent years, the field of Machine Translation has witnessed the emergence and growing popularity of Neural Machine Translation (NMT) systems, especially those constructed using transformer architectures. A critical factor in developing an effective NMT model is not just the volume, but also the quality of data. However, removing noise from parallel corpora, which involves the intricacies of two distinct languages, presents a significant challenge. In this paper, we introduce and assess a method for eliminating such noise, known as the Three-layer Denoiser. The first layer of this process, termed textual normalization, involves data cleaning using predetermined rules. The second layer incorporates a text feature extractor and a binary classifier, while the third layer evaluates the quality of sentence pairs using a pre-trained transformer model. Experimental results, obtained from training various NMT models with both clean and raw data, indicate a rise of up to 2.64 BLEU points in the models trained with sentence pairs that were filtered by the Denoiser.

Palavras-chave: neural machine translation (NMT), transformer architectures, data cleaning, three-layer denoiser, corpora denoiser


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FLORENCIO, Felipe de A.; LACERDA, Matheus S. de; CAVALCANTI, Anderson P.; ROLIM, Vitor. Three-Layer Denoiser: Denoising Parallel Corpora for NMT Systems. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 510-524. ISSN 2763-9061. DOI: