The Effect of Alignment Objectives on Code-Switching Translation
Mohamed Anwar
One of the things that need to change when it comes to machine translation is
the models' ability to translate code-switching content, especially with the
rise of social media and user-generated content. In this paper, we are
proposing a way of training a single machine translation model that is able to
translate monolingual sentences from one language to another, along with
translating code-switched sentences to either language. This model can be
considered a bilingual model in the human sense. 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. Using the WMT14
English-French (En-Fr) dataset, the trained model strongly outperforms
bidirectional baselines on code-switched translation while maintaining quality
for non-code-switched (monolingual) data.