Toward Multilingual Neural Machine Translation with Universal Encoder and Decoder
Table 1: Results of the English→German systems in an under-resourced scenario.
['[BOLD] System', '[BOLD] tst2013 BLEU', '[BOLD] tst2013 ΔBLEU', '[BOLD] tst2014 BLEU', '[BOLD] tst2014 ΔBLEU']
Compared to the baseline NMT system which is solely trained on TED English-German data, our mix-source system achieves a considerable improvement of 2.6 BLEU points on tst2013 and 2.1 BLEU points on and tst2014 . Adding French data to the source side and their corresponding German data to the target side in our mix-multi-source system also help to gain 2.2 and 1.6 BLEU points more on tst2013 tst2014, respectively. We observe a better improvement from our mix-source system compared to our mix-multi-source system. We speculate the reason that the mix-source encoder utilize the same information shared in two languages while the mix-multi-source receives and processes similar information in the other language but not necessarily the same. We might validate this hypothesis by comparing two systems trained on a common English-German-French corpus of TED. We put it in our future work’s plan.