Abstract
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.
Methodology
Generate realistic code-switching translation using the following method:
Use an alignment objective on the encoder side to create language-agnostic representations:
Paper Citation
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True Bilingual Neural Machine Translation.
Mohamed Anwar, Lekan Raheem, Maab Elrashid, Melvin Johnson, Julia Kreutzer. In ICLR 2022 Workshop AfricaNLP. [Bibtex] |