Abstract
We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of knowledge transfer. MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We categorize various approaches based on the resource scenarios as well as underlying modeling principles. We hope this paper will serve as a starting point for researchers and engineers interested in MNMT.
Abstract (translated)
近年来，我们对多语言神经机器翻译（MNMT）进行了大量的研究。由于知识转移，外语教学在提高翻译质量方面发挥了重要作用。由于端到端建模和分布式表示开辟了新的途径，因此，与统计机器翻译对应的mnmt相比，mnmt更具前景和有趣。为了利用多语种平行语料库提高翻译质量，人们提出了许多方法。然而，由于缺乏全面的调查，很难确定哪些方法是有前途的，因此值得进一步探索。在本文中，我们对现有的跨国公司文献进行了深入的调查。我们根据资源场景和基础建模原则对各种方法进行分类。我们希望本文能作为研究者和对mnmt感兴趣的工程师的起点。
URL
https://arxiv.org/abs/1905.05395
