Building a Neural Machine Translation SystemUsing Only Synthetic Parallel Data
Table 3: Translation results (BLEU) for Fr ↔ De experiments. K denotes the beam size used to generate the corresponding synthetic parallel data. The highest BLEU for each set is bold-faced.
['Corpus', 'Fr → De newstest2011', 'Fr → De newstest2012', 'Fr → De newstest2013', 'De → Fr newstest2011', 'De → Fr newstest2012', 'De → Fr newstest2013']
We note that mixing two different synthetic corpora leads to improved BLEU not their intermediate value. To investigate the cause of the improvement in PSEUDOmix, we build additional target-originated synthetic corpora for each Fr↔De translation with a beam of size 3. The improvement in BLEU, however, occurs only when mixing the source- and target-originated synthetic parallel data (b+d) compared to mixing two target-originated synthetic corpora (c+d). The same phenomenon is observed in the Fr→De case as well. The results suggest that real and synthetic sentences mixed on either side of sentence pairs enhance the capability of a synthetic parallel corpus. We conjecture that ground truth examples in both encoder and decoder networks not only compensate for the erroneous language model learned from synthetic sentences but also reinforces patterns of use latent in the pseudo sentences.