Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
Table 3: The performance of the unsupervised NMT systems with different learning objectives on en → fr newstest 2014.
['Cell', '[BOLD] Learning', '[BOLD] BLEU']
S4SS4SSS0Px3 The Effect of Comparative Translation Finally, we aim to roughly evaluate the effect of the proposed comparable translation loss in our model. Thus, we compare our model with a two-staged NMT system, where we extract and edit the parallel pairs and retrain the NMT system with the standard maximum likelihood estimation (MLE) loss in a supervised way (by taking the extracted-and-edited sentences as the ground-truth targets). We can observe that with the MLE loss, the translation performance will drop nearly 50%. The results indirectly reflect that the NMT systems are sensitive to noises in the training datasets. Meanwhile, it demonstrates by treating extracted-edit sentences as pivotal points instead of ground truth, our proposed comparative translation loss can avoid the NMT model suffers from the noise.