Correct-and-Memorize: Learning to Translate from Interactive Revisions
Table 3: The performance gain from online learning (OL) and revision memory (RM) on the NIST ZH-EN task. BiDiR is CAMIT without OL and RM .
['Model', 'NIST04', 'NIST05', 'AVE.', '\\Delta']
Additionally, we find that incorporating history learning into BiDiR can still give about 1 absolute BLEU score improvement (+0.95), which is achieved on a very strong baseline with over 50 BLEU score points. This experiment shows that learning from the interaction history is helpful to boost the translation performance. In such case, human will take less revision number for obtaining satisfactory translations.