Linguistic Input Features Improve Neural Machine Translation
Table 4: German↔English translation results with additional, synthetic training data: best perplexity on dev (newstest2013), and Bleu and chrF3 on test15 (newstest2015) and test16 (newstest2016). Bleu scores that are significantly different (p < 0.05) from respective baseline are marked with (*).
['system', 'German→English ppl ↓', 'German→English Bleu ↑', 'German→English Bleu ↑', 'German→English chrF3 ↑', 'German→English chrF3 ↑', 'English→German ppl ↓', 'English→German Bleu ↑', 'English→German Bleu ↑', 'English→German chrF3 ↑', 'English→German chrF3 ↑']
We also evaluated adding linguistic features to a stronger baseline, which includes synthetic parallel training data. For German→English, we observe similar improvements in the best development perplexity (45.2 → 44.1), test set Bleu (37.5→38.5) and chrF3 (62.2 → 62.8). Our test set We expect that linguistic input features and bidirectional decoding are orthogonal, and that we could obtain further improvements by combining the two.