Abstract
This paper describes the CUNI submission to WAT 2018 for the English-Hindi translation task using a transfer learning techniques which has proven effective under low resource conditions. We have used the Transformer model and utilized an English-Czech parallel corpus as additional data source.
Our simple transfer learning approach first trains a "parent" model for a high-resource language pair (English-Czech) and then continues the training on the low-resource (English-Hindi) pair by replacing the training corpus. This setup improves the performance compared with the baseline and in combination with back-translation of Hindi monolingual data, it allowed us to win the English-Hindi task.
The automatic scoring by BLEU did not correlate well with human judgments.