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.

本文提出了一种针对多语言机器翻译的替代方法，该方法基于特定于语言的编码器 - 解码器，可以更轻松地通过学习相应的模块来扩展到新语言。同时训练初始语言以促进通用 interlingua 表示，实验表明该方法平均优于通用编码器 - 解码器 3.28 BLEU 点，当添加新语言时，无需重新训练其余模块，因此推进了可灵活扩展的模块化多语机器翻译系统的发展。

多语言机器翻译：缩小共享编码器 - 解码器和语言特定编码器 - 解码器之间的差距

Multilingual Machine Translation: Closing the Gap between Shared and  Language-specific Encoder-Decoders

End-to-end neural machine translation has overtaken statistical machine
translation in terms of translation quality for some language pairs, specially
those with large amounts of parallel data. Besides this palpable improvement,
neural networks provide several new properties. A single system can be trained
to translate between many languages at almost no additional cost other than
training time. Furthermore, internal representations learned by the network
serve as a new semantic representation of words -or sentences- which, unlike
standard word embeddings, are learned in an essentially bilingual or even
multilingual context. In view of these properties, the contribution of the
present work is two-fold. First, we systematically study the NMT context
vectors, i.e. output of the encoder, and their power as an interlingua
representation of a sentence. We assess their quality and effectiveness by
measuring similarities across translations, as well as semantically related and
semantically unrelated sentence pairs. Second, as extrinsic evaluation of the
first point, we identify parallel sentences in comparable corpora, obtaining an
F1=98.2% on data from a shared task when using only NMT context vectors. Using
context vectors jointly with similarity measures F1 reaches 98.9%.

该研究系统研究了端到端的神经机器翻译的上下文向量作为句子的交互语言表征的功效，并在可比较语料库中取得了较高的平行句子识别准确率。