Controllable Invariance through Adversarial Feature Learning
Table 1: Results on multi-lingual machine translation.
['Model', 'test (fr-en)', 'test (de-en)']
The encoder-decoder trained on multi-lingual data employs a single encoder for both source languages. Firstly, both multi-lingual systems outperform the bilingual encoder-decoder even though multi-lingual systems use similar number of parameters to translate two languages, which shows that learning invariant representation leads to better generalization in this case. The better generalization may be due to transferring statistical strength between data in two languages.