Lexically Constrained Neural Machine Translation with Levenshtein Transformer
Table 3: Comparison to previous work. Baseline Transformer and Post18 results are from dinu-etal-2019-training.
['[EMPTY]', 'Wiktionary Term%', 'Wiktionary BLEU', 'IATE Term%', 'IATE BLEU']
We evaluate each approach on the WMT’17 En-De test set with constraint terms from Wiktionary and IATE dictionaries. Note that our baseline LevT model with Transformer blocks of 6 layers is superior to that of dinu-etal-2019-training who used a 2-layer configuration. Despite having a stronger baseline, we obtain higher absolute BLEU score improvements (0.96 and 1.16 BLEU on Wiktionary and IATE, respectively) and achieved 100% term usage. We report additional experiments on WMT’16 Romanian-English news translation task bojar-etal-2016-findings in the Appendix.