Dual Learning for Machine Translation
Table 2: Reconstruction performance of En↔Fr task
['[EMPTY]', 'En→Fr→En (L)', 'Fr→En→Fr (L)', 'En→Fr→En (S)', 'Fr→En→Fr (S)']
We study the self-reconstruction performance of the algorithms: For each sentence in the test set, we translated it forth and back using the models and then checked how close the back translated sentence is to the original sentence using the BLEU score. We also used beam search to generate all the translation results. In particular, our proposed method outperforms NMT by about 11.9/9.6 points when using warm-start model trained on large parallel data, and outperforms NMT for about 20.7/17.8 points when using the warm-start model trained on 10% parallel data.