On the Importance of Word Boundaries in Character-level Neural Machine Translation
Table 3: Experiment results in the English-to-German direction with WMT data sets. Translation accuracy is measured with BLEU. Best scores are in bold font.
['[BOLD] Model', '[BOLD] newstest15']
Studies have shown that character-level NMT models could potentially reach the same performance with the subword-based NMT models cherry2018revisiting, although this might require increasing the capacity of the network. On the other hand, the consistency in the accuracy obtained using the hierarchical decoding model from low to mid resource settings suggests that explicit modeling of word boundaries aids in achieving a more efficient solution to character-level translation.