Exploiting Multilingualism through Multistage Fine-Tuning for Low-Resource Neural Machine Translation

IJCNLP 2019 Raj DabreAtsushi FujitaChenhui Chu

This paper highlights the impressive utility of multi-parallel corpora for transfer learning in a one-to-many low-resource neural machine translation (NMT) setting. We report on a systematic comparison of multistage fine-tuning configurations, consisting of (1) pre-training on an external large (209k{--}440k) parallel corpus for English and a helping target language, (2) mixed pre-training or fine-tuning on a mixture of the external and low-resource (18k) target parallel corpora, and (3) pure fine-tuning on the target parallel corpora... (read more)

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