Machine Translation of Low-Resource Indo-European Languages

• URL: http://arxiv.org/abs/2108.03739v1
• Date: Sun, 8 Aug 2021 21:41:08 GMT
• Title: Machine Translation of Low-Resource Indo-European Languages
• Abstract summary: We build two systems to study how relatedness can benefit the translation performance. The primary system adopts machine translation model pre-trained on related language pair and the contrastive system adopts that pre-trained on unrelated language pair.
• Score: 7.113332213566949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transfer learning has been an important technique for low-resource neural machine translation. In this work, we build two systems to study how relatedness can benefit the translation performance. The primary system adopts machine translation model pre-trained on related language pair and the contrastive system adopts that pre-trained on unrelated language pair. We show that relatedness is not required for transfer learning to work but does benefit the performance.

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