Lexically Constrained Neural Machine Translation with Levenshtein Transformer
Table 5: Results of LevT with lexical constraints on WMT16 Ro-En task
['[EMPTY]', 'Term%', 'BLEU Full', 'BLEU Constr.', 'Speed (sent/sec)']
We learn a joint BPE vocabulary with 40,000 operations, which results in 39,348 vocabulary size. We observe consistent findings in our En-De experiments in terms of improved term usage rate (from 80% to 100%) and a small margin of improvement of 0.7 BLEU, while being able to decode as fast as a baseline LevT model.