Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages

Understand

Transfer learning approaches for Neural Machine Translation (NMT) train a NMT model on the assisting-target language pair (parent model) which is later fine-tuned for the source-target language pair of interest (child model), with the target language being the same.

• In many cases, the assisting language has a different word order from the source language.
• We show that divergent word order adversely limits the benefits from transfer learning when little to no parallel corpus between the source and target language is available.
• To bridge this divergence, We propose to pre-order the assisting language sentence to match the word order of the source language and train the parent model.