Handling Syntactic Divergence in Low-resource Machine Translation

EMNLP/IJCNLP (1), pp. 1388-1394, 2019.

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Experimental results on ja-en and ug-en translations show that our approach achieves significant improvements over baseline systems, demonstrating the effectiveness of the proposed approach on divergent language pairs

Abstract:

Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs. Data augmentation methods such as back-translation make it possible to use monolingual data to help alleviate these issues, but back-translation itself fai...More

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