Transfer Learning-Based Neural Machine Translation for Low-Resource Languages

Abstract

College of Liberal Arts, Ludong University, Yantai 264025, China Chinese Lexicography Research Center, Yantai 264025, China Neural Machine Translation (NMT) improves readability by augmenting sentence suggestions based on the precise likelihood of the words. The word suggestions are trained using learning paradigms through repeated translations, word searches, and user inputs. However, the challenging process is the implication of NMT for low-resource language wherein the chances of false suggestions/ word substitutions are high. So, this article proposes a Likelihood-based Machine Translation Model (LMTM) for low-resource languages. The model uses word frequency and potential substitutions from less-known sentences to identify sentences with high precision. This is achieved through a combination of recurrence and substitutions using transfer learning. The identified high-likelihood words are used for sentence augmentation, and the entire set of words from the generated sentence updates the learning paradigm. The model suggests the highest likelihood words for NMT, preventing sentence falsification and ensuring accurate translations. The proposed model increases likelihood by 9.15%, correctness by 7.46%, and substitutions by 7.7%, respectively. It reduces falsification and time complexity by 9.33% and 8.52%, respectively. Overall, the LMTM improves translation quality for low-resource languages.