Synthetic Source Language Augmentation for Colloquial Neural Machine Translation

• URL: http://arxiv.org/abs/2012.15178v1
• Date: Wed, 30 Dec 2020 14:52:15 GMT
• Title: Synthetic Source Language Augmentation for Colloquial Neural Machine Translation
• Authors: Asrul Sani Ariesandy, Mukhlis Amien, Alham Fikri Aji, Radityo Eko Prasojo
• Abstract summary: We develop a novel colloquial Indonesian-English test-set collected from YouTube transcript and Twitter. We perform synthetic style augmentation to the source of formal Indonesian language and show that it improves the baseline Id-En models.
• Score: 3.303435360096988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural machine translation (NMT) is typically domain-dependent and style-dependent, and it requires lots of training data. State-of-the-art NMT models often fall short in handling colloquial variations of its source language and the lack of parallel data in this regard is a challenging hurdle in systematically improving the existing models. In this work, we develop a novel colloquial Indonesian-English test-set collected from YouTube transcript and Twitter. We perform synthetic style augmentation to the source of formal Indonesian language and show that it improves the baseline Id-En models (in BLEU) over the new test data.

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