论文标题
MALM：混合零击机翻译的增强语言建模
MALM: Mixing Augmented Language Modeling for Zero-Shot Machine Translation
论文作者
论文摘要
大型预训练的语言模型在NLP中取得了显着进步。预培训和微调在文本处理中的任务中都提供了最先进的性能。数据增强技术还有助于在低或零资源任务上建立最先进的模型。过去，许多作品都试图学习单个大规模杂化的机器翻译模型，以用于零拍。尽管这些翻译模型正在产生正确的翻译，但主要的挑战是这些模型正在生产错误的语言，以零拍。这项工作及其结果表明，迅速的大型模型不会遭受脱离目标语言错误，即由于转换为错误的语言而引起的错误。我们从经验上证明了自我监督的预训练和数据增强的有效性。
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.
