Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
Table 3: Results of the German→French systems in a zero-resourced scenario.
['System', 'BLEU', 'ΔBLEU']
We evaluate bridge and universal systems on two German→French test sets. They are compared to a direct system, which is an NMT trained on German→French data, and to a pivot system, which essentially consists of two separate NMTs trained to translate from German to English and English to French. The direct system should not exist in a real zero-resourced situation. We refer it as the perfect system for comparison purpose only. In case of the pivot system, to generate a translated text in French from a German sentence, we first translate it to English, then the output sentence is fed to the English→German NMT system to obtain the French translation. Since there are more than two languages involved in those systems, we increase the number of BPE merging operations proportionally in order to reduce the number of rare words in such systems. We do not expect our proposed systems to perform well with this primitive way of building direct translating connections since this is essentially a difficult task.