Assessing the Bilingual Knowledge Learned by Neural Machine Translation Models
Table 3: Comparison of phrases from three capacities.
['[BOLD] Model', '[BOLD] Shared  [ITALIC] Size', '[BOLD] Shared  [ITALIC] BLEU', '[BOLD] Non-Shared  [ITALIC] Size', '[BOLD] Non-Shared  [ITALIC] BLEU']
The phrases shared by all models take the overwhelming majority, which add most value to the translation performance. We conjecture that enlarging capacity improves NMT performance by better exploiting complex patterns beyond bilingual lexicons. This also confirms our intuition that bilingual lexicons can be a crucial early step in assessing the knowledge in NMT models.