Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection
Table 7: LM method does not denoise, but NMT method (proposed) does; and a denoised model has improved general translation accuracy. P1+NMT 20%: fine-tune P1 with top 20% selection by NMT method. P1+NNLM 20%: fine-tune P1 with top 20% selection by NNLM method.
['System', 'n2014', 'd2015', 'patent']
The NNLM method does not discern noise and thus the top selection would be as noisy as the baseline data. As a result, fine-tuning the noisy baseline (P1) would not improve. As a matter of fact, the patent BLEU drops over baseline, probably indicating that domain data selection causes data bias. The proposed method, on the other hand, performs clearly better (P8), for example, compared to P9, +2.5 BLEU on n2014, +3.8 BLEU on d2015 and +10.4 BLEU on patent. These prove the effectiveness of the proposed method in producing better systems on noisy data.