From Bilingual to Multilingual Neural Machine Translation by Incremental Training
Table 1: Experiment results measured in BLEU score. All blank positions are not tested or not viable combinations with our data.
['[BOLD] System', '[BOLD] ES-EN', '[BOLD] EN-ES', '[BOLD] FR-EN', '[BOLD] DE-EN']
Our first experiment consists in comparing the performance of the jointly trained system to the standard Transformer. As explained in previous sections, this joint model is trained to perform two different tasks, auto-encoding and translation in both directions. In our experiments, these directions are Spanish-English and English-Spanish. In auto-encoding, both languages provide good results at 98.21 and 97.44 BLEU points for English and Spanish, respectively. In translation, we observe a decrease in performance. This difference suggests that even though the encoders and decoders of the system are compatible they still present some differences in the internal representation. Our second experiment consists of incrementally adding different languages to the system, in this case, German and French. Note that, since we freeze the weights while adding the new language, the order in which we add new languages does not have any impact on performance. French-English is closer to the baseline performance and this may be due to its similarity to Spanish, one of the languages of the initial system languages.