Transfer Learning for Low-Resource Neural Machine Translation
Table 2: Our method significantly improves NMT results for the translation of low-resource languages into English. Results show test-set Bleu scores. The ‘NMT’ column shows results without transfer, and the ‘Xfer’ column shows results with transfer. The ‘Final’ column shows Bleu after we ensemble 8 models and use unknown word replacement.
['Language', 'SBMT', 'NMT', 'Xfer', 'Final']
The parent models were trained on the WMT 2015 Our baseline NMT systems (‘NMT’ column) all receive a large Bleu improvement when using the transfer method (the ‘Xfer’ column) with an average Bleu improvement of 5.6. Additionally, when we use unknown word replacement from \newciteluong-EtAl:2015:ACL-IJCNLP and ensemble together 8 models (the ‘Final’ column) we further improve upon our Bleu scores, bringing the average Bleu improvement to 7.5. Overall our method allows the NMT system to reach competitive scores and beat the SBMT system in one of the four language pairs.