Overcoming the Rare Word Problem for Low-Resource Language Pairs in Neural Machine Translation
Table 4: The number of rare words in which their affixes are detached from the English texts in the SAA algorithm.
['[EMPTY]', 'Train', 'tst2012', 'tst2013']
For comparison purpose, English texts are split into sub-words using Sennrich’s BPE methods. We observe that, the achieved BLEU points are lower Therefore, we then apply the SAA algorithm on the English texts from (2) in the English→Vietnamese. The improvement in BLEU are +0.74 between (4) and (1).