Neural machine translation for low-resource languages
This addresses the challenge of machine translation for low-resource languages, but it is incremental as SMT remains the best option in such settings.
The paper tackled the problem of neural machine translation for low-resource languages, showing that their method can produce acceptable translations with only 70,000 tokens of training data, where baseline NMT fails.
Neural machine translation (NMT) approaches have improved the state of the art in many machine translation settings over the last couple of years, but they require large amounts of training data to produce sensible output. We demonstrate that NMT can be used for low-resource languages as well, by introducing more local dependencies and using word alignments to learn sentence reordering during translation. In addition to our novel model, we also present an empirical evaluation of low-resource phrase-based statistical machine translation (SMT) and NMT to investigate the lower limits of the respective technologies. We find that while SMT remains the best option for low-resource settings, our method can produce acceptable translations with only 70000 tokens of training data, a level where the baseline NMT system fails completely.