Compositional Representation of Morphologically-Rich Inputfor Neural Machine Translation
Table 4: Experiment results. Best scores for each translation direction are in bold font. All improvements over the baseline (simple model with BPE) are statistically significant (p-value 
['[BOLD] Model', '[BOLD] Vocabulary  [BOLD] Units', '[BOLD] Input  [BOLD] Representations', '[BOLD] BLEU  [BOLD] Tr-En', '[BOLD] BLEU  [BOLD] Ar-En', '[BOLD] BLEU  [BOLD] Cs-En', '[BOLD] BLEU  [BOLD] De-En', '[BOLD] BLEU  [BOLD] It-En']
With the simple model, LMVR based units achieve the best accuracy in translating all languages, with improvements over BPE by 0.85 to 1.09 BLEU points in languages with high morphological complexity (Arabic, Czech and Turkish) and 0.32 to 0.53 BLEU points in languages with low to medium complexity (Italian and German). Moreover, simple models using character trigrams as vocabulary units reach much higher translation accuracy compared to models using characters, indicating their superior performance in handling contextual ambiguity. In the Italian to English translation direction, the performance of simple models using character trigrams and BPE sub-word units as input representations are almost comparable, showing that character trigrams can even be sufficient as the stand-alone vocabulary units in languages with low lexical sparseness. These findings suggest that each type of sub-word unit used in the simple model is specifically convenient for a given morphological typology.