Target-side Word Segmentation Strategies for Neural Machine Translation

This is the authors' abstract. We don't add key points for this paper.

For efficiency considerations, state-of-theart neural machine translation (NMT) requires the vocabulary to be restricted to a limited-size set of several thousand symbols. This is highly problematic when translating into inflected or compounding languages. A typical remedy is the use of subword units, where words are segmented into smaller components. Byte pair encoding, a purely corpus-based approach, has proved effective recently.