Multilingual machine translation (MMT), trained on a mixture of parallel and monolingual data, is key for improving translation in low-resource language pairs. However, the literature offers conflicting results on the performance of different methods. To resolve this, we examine how denoising autoencoding (DAE) and backtranslation (BT) impact MMT under different data conditions and model scales. Unlike prior studies, we use a realistic dataset of 100 directions and consider many domain combinations of monolingual and test data. We find that monolingual data generally helps MMT, but models are surprisingly brittle to domain mismatches, especially at smaller model scales. BT is beneficial when the parallel, monolingual, and test data sources are similar but can be detrimental otherwise, while DAE is less effective than previously reported. Next, we analyze the impact of scale (from 90M to 1.6B parameters) and find it is important for both methods, particularly DAE. As scale increases, DAE transitions from underperforming the parallel-only baseline at 90M to converging with BT performance at 1.6B, and even surpassing it in low-resource. These results offer new insights into how to best use monolingual data in MMT.

通过实验研究了多种语言机器翻译在不同数据条件和模型尺度下，去噪自编码器和回译技术对多种语言机器翻译的影响、以及单语数据在多种语言机器翻译中的作用和数据规模对机器翻译的影响等问题。结果发现单语数据总体有益于机器翻译，但在小型模型中对领域不匹配，BT对相似的平行，单语和测试数据来源是有益的，但在其他情况下会有不利影响，DAE相对于以前报告的效果不如。规模永远是关键。

单语数据何时对多语言翻译有帮助：领域和模型规模的作用