Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation
Zhong Zhou, Matthias Sperber, Alexander H. Waibel · 2018
We work on translation from rich-resource languages to low-resource languages.The main challenges we identify are the lack of lowresource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems.We build a translation system that addresses these challenges using eight European language families as our test ground.Firstly, we add the source and the target family labels and study intra-family and inter-family influences for effective cross-lingual transfer.We achieve an improvement of +9.9 in BLEU score for English-Swedish translation using eight families compared to the single-family multi-source multi-target baseline.Moreover, we find that training on two neighboring families closest to the low-resource language is often enough.Secondly, we construct an ablation study and find that reasonably good results can be achieved even with considerably less target data.Thirdly, we address the variable-binding problem by building an order-preserving named entity translation model.We obtain 60.6% accuracy in qualitative evaluation where our translations are akin to human translations in a preliminary study.