Unsupervised Domain Adaptation for Neural Machine Translation with Domain-Aware Feature Embeddings
Table 1: Translation accuracy (BLEU) under different settings. The second and third rows list source and target domains respectively. “DAFE w/o Embed” denotes DAFE without embedding learners and “Back-DAFE” denotes back-translation by target-to-source model trained with DAFE. DAFE outperforms other approaches when adapting between domains (row 1-5, column 2-7) and is complementary to back-translation (row 6-8).
['[BOLD] Method', '[BOLD] Method', '[BOLD] German-English LAW', '[BOLD] German-English LAW', '[BOLD] German-English MED', '[BOLD] German-English MED', '[BOLD] German-English IT', '[BOLD] German-English IT', '[BOLD] Czech-English WMT', '[BOLD] Czech-English WMT', '[BOLD] Czech-English WMT', '[BOLD] German-English WMT', '[BOLD] German-English WMT', '[BOLD] German-English WMT']
The copy method (row 2) and back translation (row 3) both improve the model significantly, with back-translation being a better alternative to copying. DAFE (row 5) achieves superior performance compared to back-translation, with improvements of up to 2 BLEU points. Also, removing the embedding learners leads to degraded performance (row 4), indicating the necessity of their existence.