![]() ![]() However bilingual fine-tuning does not leverage the full capacity of multilingual pretraining. MBART-25 is fine-tuned on bitext corpus (parallel corpus of one language pair) to develop MT models. Let us see a brief overview of each of MT models. ![]() Pretraining on non-English centric parallel data helps to model to perform well in non-English translation directions also. The parallel data used to pretrain these models are non-English centric i.e., one of the sentences in the sentence pair need not be English. To overcome the drawbacks in English centric models, non-English centric models like M2M100 and NLLB200 are developed.The main drawback with English centric models is that their lower performance for non-English translation directions. mBART50-based models are example of English Centric. ![]() English centric parallel data consists of pairs of text sequences in which one text sequence is in English and the other sequence can be from any of the supported languages.
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