True Bilingual Neural Machine Translation
Mohamed Anwar     Lekan Raheem     Maab Elrashid     Melvin Johnson    
Julia Kreutzer    
African Institute for Mathematical Sciences (AIMS)     Google Research    
[Paper]     [GitHub]     [Poster]     [Slides]    


Bilingual machine translation permits training a single model that translates monolingual sentences from one language to another. However, a model is not truly bilingual unless it can translate back and forth in both language directions it was trained on, along with translating code-switched sentences to either language. We propose a true bilingual model trained on WMT14 English-French (En-Fr) dataset. For better use of parallel data, we generated synthetic code-switched (CSW) data along with an alignment loss on the encoder to align representations across languages. Our model strongly outperforms bilingual baselines on CSW translation while maintaining quality for non-code switched data.


Generate realistic code-switching translation using the following method:

Use an alignment objective on the encoder side to create language-agnostic representations:

Paper Citation

True Bilingual Neural Machine Translation.

Mohamed Anwar, Lekan Raheem, Maab Elrashid, Melvin Johnson, Julia Kreutzer.

In ICLR 2022 Workshop AfricaNLP.