MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation

• URL: http://arxiv.org/abs/2210.00320v1
• Date: Sat, 1 Oct 2022 17:01:30 GMT
• Title: MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation
• Authors: Kshitij Gupta
• Abstract summary: Large pre-trained language models have brought remarkable progress in NLP. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large pre-trained language models have brought remarkable progress in NLP. Pre-training and Fine-tuning have given state-of-art performance across tasks in text processing. Data Augmentation techniques have also helped build state-of-art models on low or zero resource tasks. Many works in the past have attempted at learning a single massively-multilingual machine translation model for zero-shot translation. Although those translation models are producing correct translations, the main challenge is those models are producing the wrong languages for zero-shot translation. This work and its results indicate that prompt conditioned large models do not suffer from off-target language errors i.e. errors arising due to translation to wrong languages. We empirically demonstrate the effectiveness of self-supervised pre-training and data augmentation for zero-shot multi-lingual machine translation.

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