Domain Adaptation of Neural Machine Translation by Lexicon Induction
Table 7: BLEU scores of LSTM based NMT models when trained on WMT14 De-En data (Base), and adapted to one domain (DALI). The last two columns show the percentage of source word/subword overlap between the training data on the WMT domain and other five domains.
['Domain', 'Base', 'DALI', 'Word', 'Subword']
We further test the adaptation performance of DALI when we train our base NMT model on the WMT14 German-English parallel corpus. The corpus is a combination of Europarl v7, Common Crawl corpus and News Commentary, and consists of 4,520,620 parallel sentences from a wider range of domains. U. We also show the percentage of source words or subwords in the training corpus of five domains being covered by the WMT14 corpus. Although the unadapted NMT system trained on the WMT14 corpus obtains higher scores than that trained on the corpus of each individual domain, DALI still improves the adaptation performance over the unadapted NMT system by up to 5 BLEU score.