Target-side Word Segmentation Strategies for Neural Machine Translation
Matthias Huck, Simon Riess, Alexander Fraser
WMT 2017

For efficiency considerations, state-of-the-art 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.

In this paper, we investigate word segmentation strategies that
incorporate more linguistic knowledge. We demonstrate that
linguistically informed target word segmentation is better suited for
NMT, leading to improved translation quality on the order of magnitude
of +0.5 BLEU and -0.9 TER for a medium-scale English-to-German
translation task.

Our work is important in that it shows that linguistic knowledge can
be used to improve NMT results over results based only on the
language-agnostic byte pair encoding vocabulary reduction technique.