Word-based Domain Adaptation for Neural Machine Translation
Table 4: Study on the effect of different smoothing methods for word weights generation. Baseline is the same as before. w.w. without smoothing means the word weights (w.w.) are computed without smoothing in the log domain. w.w. (mean smooth.) indicates smoothing the word scores via using a mean average filter before thresholding and w.w. (gauss. smooth.) indicates using a normal distributed filter before thresholding. The approaches regarding different smoothing methods are described in Section 2.2.
['System', 'BLEU [%]', 'TER [%]']
The word weights generated without using smoothing methods, where ^st=st, lead to poor translation quality of 21.38% from 24.37% BLEU and 66.25% from 61.66% TER, respectively. We need to smooth the word scores before thresholding because the values of logPI(yt|yt−1t−n)−logPO(yt|yt−1t−n) are noisy. If there are selected isolated words like ’,’ which have higher scores than the surrounding text, it may cause rare vocabulary problem after training.