论文标题
多语言机器翻译：缩小共享和特定语言编码器之间的差距
Multilingual Machine Translation: Closing the Gap between Shared and Language-specific Encoder-Decoders
论文作者
论文摘要
最先进的多语言计算机翻译依赖于通用编码器码头，该通用编码器需要重新训练整个系统以添加新语言。在本文中，我们提出了一种基于语言特定编码器描述器的替代方法，因此可以通过学习相应的模块更容易地扩展到新语言。为了鼓励共同的interlingua代表，我们同时训练n个初始语言。我们的实验表明，所提出的方法的表现平均超过了3.28个BLEU点，并且在添加新语言时，而无需重新训练其余模块。总而言之，我们的工作缩小了共享和特定语言编码器描述器之间的差距，朝着模块化多语言的机器翻译系统前进，这些系统可以灵活地扩展到终身学习设置中。
State-of-the-art multilingual machine translation relies on a universal encoder-decoder, which requires retraining the entire system to add new languages. In this paper, we propose an alternative approach that is based on language-specific encoder-decoders, and can thus be more easily extended to new languages by learning their corresponding modules. So as to encourage a common interlingua representation, we simultaneously train the N initial languages. Our experiments show that the proposed approach outperforms the universal encoder-decoder by 3.28 BLEU points on average, and when adding new languages, without the need to retrain the rest of the modules. All in all, our work closes the gap between shared and language-specific encoder-decoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.
