Correct-and-Memorize: Learning to Translate from Interactive Revisions
Table 2: Average BLEU score of our CAMIT’s bi-directional decoder after multiple interactions on the NIST ZH-EN task.
['Revisions', '1', '2', '3', '4']
We also apply BiDiR decoder on Transformer with the same setting of RNNSearch. By revision once, BiDiR achieves an absolute BLEU improvement of 6.93, upon a strong Transformer baseline (43.65 to 50.58), significantly outperforming UniDiR (+2.04) and \text{UniDiR}_{\text{G}} (+4.57). Results show that although the baseline of Transformer is significantly better than RNNSearch, both of them give pretty high BLEU scores (around 60) after 4 revisions. RNNSearch even obtains higher BLEU scores. For convenience, we will only report results based on RNNSearch in followed experiments.