  In this work, we explore the usefulness of target factors in neural machine
translation (NMT) beyond their original purpose of predicting word lemmas and
their inflections, as proposed by Garc\`ia-Mart\`inez et al., 2016. For this,
we introduce three novel applications of the factored output architecture: In
the first one, we use a factor to explicitly predict the word case separately
from the target word itself. This allows for information to be shared between
different casing variants of a word. In a second task, we use a factor to
predict when two consecutive subwords have to be joined, eliminating the need
for target subword joining markers. The third task is the prediction of special
tokens of the operation sequence NMT model (OSNMT) of Stahlberg et al., 2018.
Automatic evaluation on English-to-German and English-to-Turkish tasks showed
that integration of such auxiliary prediction tasks into NMT is at least as
good as the standard NMT approach. For the OSNMT, we observed a significant
improvement in BLEU over the baseline OSNMT implementation due to a reduced
output sequence length that resulted from the introduction of the target
factors.

Computation and Language;Neural and Evolutionary Computing