Dual Learning: Theoretical Study and an Algorithmic Extension
Table 2: BLEU scores of WMT2014 En↔De and MultiUN En↔Fr translations tasks.
['[EMPTY]', 'En→De', 'De→En', 'En→Fr', 'Fr→En']
Translation qualities. We can see that after applying dual learning, the performances of all tasks are boosted. Specifically, on En→De and De→En translation tasks, we can boost the baseline from 29.79 to 32.18 (2.39 points improvement), and from 34.15 to 38.06 (3.91 points improvement). On the other task, dual learning can achieve 0.65 and 0.86 point improvement, which demonstrates its effectiveness. We found that on MultiUN, we do not achieve as much improvement as WMT. The reason is that the MultiUN dataset is a collection of translated documents from the United Nations, which are usually of formal and simple patterns that are easy to learn. As a result, introducing more data might not increase the BLEU so much.