Dual Learning for Machine Translation
Table 1: Translation results of En↔Fr task. The results of the experiments using all the parallel data for training are provided in the first two columns (marked by “Large”), and the results using 10% parallel data for training are in the last two columns (marked by “Small”).
['[EMPTY]', 'En→Fr (Large)', 'Fr→En (Large)', 'En→Fr (Small)', 'Fr→En (Small)']
We report the experimental results in this section. Recall that the two baselines for English→French and French→English are trained separately while our dual-NMT conducts joint training. For the translation from English to French, dual-NMT outperforms the baseline NMT by about 2.1/3.4 points for the first/second warm start setting, and outperforms pseudo-NMT by about 1.7/3.1 points for both settings. For the translation from French to English, the improvement is more significant: our dual-NMT outperforms NMT by about 2.3/5.2 points for the first/second warm start setting, and outperforms pseudo-NMT by about 2.1/4.3 points for both settings. Surprisingly, with only 10% bilingual data, dual-NMT achieves comparable translation accuracy as vanilla NMT using 100% bilingual data for the Fr→En task. These results demonstrate the effectiveness of our dual-NMT algorithm. Furthermore, we have the following observations: