Guiding Neural Machine Translation with Retrieved Translation Pieces
Table 10: Countγ
['[ITALIC] γ', '[EMPTY]', '0', '1', '2', '5', '10', '20', '50', '100']
As we can see, our method helped little for the translation of n-grams that do not occur in the training set, which is reasonable because we only reward n-grams that occur in the training set. However, our method helped significantly for the translation of n-grams that do occur in the training set but are infrequent (occur less than 5 times). As the frequency of n-grams increases, the improvement caused by our method decreased. We analyze that the reason why our method is especially helpful for infrequent n-grams is that NMT is trained on the whole training corpus for maximum likelihood and tends to generate more frequent n-grams while our method computes scores for the collected translation pieces based on sentence similarities and does not prefer more frequent n-grams.