Fully Character-Level Neural Machine Translation without Explicit Segmentation

Understand

Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens.

• We introduce a neural machine translation (NMT) model that maps a source character sequence to a target character sequence without any segmentation.
• We employ a character-level convolutional network with max-pooling at the encoder to reduce the length of source representation, allowing the model to be trained at a speed comparable to subword-level models while capturing local regularities.
• Our character-to-character model outperforms a recently proposed baseline with a subword-level encoder on WMT'15 DE-EN and CS-EN, and gives comparable performance on FI-EN and RU-EN.