Phrase-Based & Neural Unsupervised Machine Translation
Таблица 3: Fully unsupervised results. We report the BLEU score for PBSMT, NMT, and their combinations on 8 directed language pairs. Results are obtained on newstest 2014 for en−fr and newstest 2016 for every other pair.
['[EMPTY]', 'en → fr', 'fr→ en', 'en→ de', 'de→ en', 'en→ ro', 'ro→ en', 'en→ ru', 'ru→ en']
For instance, the fr→en model obtains a BLEU score of 17.5 at iteration 0 – i.e. after the unsupervised phrase table construction – while it achieves a score of 27.2 at iteration 4. This highlights the importance of multiple back-translation iterations. We simply add the data generated by the unsupervised PBSMT system to the back-translated data produced by the NMT model. By combining PBSMT and NMT, we achieve BLEU scores of 20.2 and 25.2 on the challenging en→de and de→en translation tasks. While we also tried bootstraping the PBSMT model with back-translated data generated by a NMT model (NMT + PBSMT), this did not improve over PBSMT alone.