Towards one-shot learning for rare-word translation with external experts
Table 3: The results of German→English on various domains: TEDTalks and Biomedical. We use AN for using annotations from the phrase table, RF for using REINFORCE (α= 0.5) and CP for using the Copy mechanism.
['System—Data', '[BOLD] dev2010  [BOLD] BLEU', '[BOLD] dev2010  [BOLD] SAC', '[BOLD] dev2010  [BOLD] SUG', '[BOLD] tst2010  [BOLD] BLEU', '[BOLD] tst2010  [BOLD] SAC', '[BOLD] tst2010  [BOLD] SUG', '[BOLD] HIML  [BOLD] BLEU', '[BOLD] HIML  [BOLD] SAC', '[BOLD] HIML  [BOLD] SUG']
On the dev2010 and tst2010 in-domain datasets, we observe similar phenomena to the En-Es direction. Rare-word performance increases with the number of words copied, and the combination of the copy mechanism and REINFORCE help us copy consistently. Surprisingly, however, the BLEU score drops with annotations. This may be because of the relative morphologically complexity of the German words compared to the English, making it harder to generate the correct word form.