Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation
Table 6: Combining Approaches
['[BOLD] experiment', '[BOLD] de-en', '[BOLD] es-en']
It is trivial to combine WoW with other augmentation approaches like COPY and BT. We hypothesized that combination with COPY is most promising, given that Currey et al. Specifically, COPY helps the model copy unchanged words like named entities. We explore such combinations by running experiments for WoW + COPY and WoW + BT As expected, combination with COPY performs better since the copied target sentences contains words like named entities which are entirely missing from the bilingual dictionaries. Interestingly, 10k WoW + 10k COPY outperforms using 20k WoW for es-en, as the model is able to draw upon the complimentary benefits of both methods. On the other hand, combining our method with BT leads to a decrease in performance, which goes on to show that a low quality BT model does not add any complementary benefit.