On the Importance of Word Boundaries in Character-level Neural Machine Translation
Table 1: Results of the evaluation of models in translating languages with different morphological typology using the IWSLT data sets. The average number of parameters are calculated only for the decoders of the NMT models at a resolution of millions (M). The best scores for each translation direction are in bold font. All improvements over the baselines are statistically significant (p-value 
['[BOLD] Model', '[BOLD] BLEU  [BOLD] AR', '[BOLD] BLEU  [BOLD] CS', '[BOLD] BLEU  [BOLD] DE', '[BOLD] BLEU  [BOLD] IT', '[BOLD] BLEU  [BOLD] TR', '[BOLD] Avg. Num.  [BOLD] Params']
The improvements are especially evident in Arabic and Turkish, languages with the most complex morphology, where the accuracy with the hierarchical decoder is 1.28 and 1.22 BLEU points higher, respectively, and comparable in Czech, Italian and German, which represent the fusional languages. In Czech, the hierarchical model outperforms the subword-based model by 0.19 BLEU and in Italian by 0.41 BLEU points. The subword-based NMT model achieves the best performance in German, a language that is rich in compounding, where explicit subword segmentation might allow learning better representations for translation units.