Publication at Faculty of Mathematics and Physics |
2021
Abstract
The Covid-19 pandemic has created a global demand for accurate and up-to-date information which often originates in English and needs to be translated. To train a machine translation system for such a narrow topic, we leverage in-domain training data in other languages both from related and unrelated language families.
We experiment with different transfer learning schedules and observe that transferring via more than one auxiliary language brings the most improvement. We compare the performance with joint multilingual training and report superior results of the transfer learning approach.