Bilingual Dictionary Based Neural Machine Translation without Using Parallel Sentences
Table 3: Precision of Discovered New Word Pairs.
['[EMPTY]', 'MuseUnsupervised', 'MuseSupervised', 'UNMT+SWET', 'UNMT+UWET', 'AT', 'Bi-view AT']
Muse induces new word pairs through either the supervised way or the unsupervised way. MuseSupervised is better than MuseUnsupervised since it is supervised by the ground-truth bilingual dictionary. Our AT/Bi-view AT surpasses MuseSupervised by a large margin. UNMT+SWET/UWET also obtains good performance through the word embedding transformation. Bi-view AT significantly surpasses UNMT+SWET/UWET in precision@5 and precision@10, while is worse than them in precision@1. This indicates that Bi-view AT can produce better n-best translation words that are beneficial for NMT beam decoding to find better translations.