Analysis of Rule-Based Machine Translation and Neural Machine Translation Approaches for Translating Portuguese to LIBRAS
Abstract
In this paper, we propose a rule-based machine translator for Brazilian Portuguese to Brazilian Sign Language translation. This translator was implemented using the part of speech tagging and lemmatization techniques with implementations in Aelius and CoGrOO, respectively. Then, we developed a convolutional translator seeking to replicate the rule-based translation with a sintetic corpus. In this corpus, preprocessing techniques such as substitution of names, numbers and spelling errors by symbols were applied to improve processing. The translator were tested on two corpus, Bosque e OpenSub (extracted from a site of subtitles), of 69 and 36.858 lines respectively, and compared with translations generated by interpreters and translations generated by the application VLibras (LAVID-UFPB).
Keywords:
sign language, machine translation, neural machine translation
Published
2019-10-29
How to Cite
OLIVEIRA, Caio César M. de; RÊGO, Thaís G. do; LIMA, Manuella A. C. B.; ARAÚJO, Tiago Maritan U..
Analysis of Rule-Based Machine Translation and Neural Machine Translation Approaches for Translating Portuguese to LIBRAS. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 25. , 2019, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 117-124.
