  Lexically constrained machine translation allows the user to manipulate the
output sentence by enforcing the presence or absence of certain words and
phrases. Although current approaches can enforce terms to appear in the
translation, they often struggle to make the constraint word form agree with
the rest of the generated output. Our manual analysis shows that 46% of the
errors in the output of a baseline constrained model for English to Czech
translation are related to agreement. We investigate mechanisms to allow neural
machine translation to infer the correct word inflection given lemmatized
constraints. In particular, we focus on methods based on training the model
with constraints provided as part of the input sequence. Our experiments on the
English-Czech language pair show that this approach improves the translation of
constrained terms in both automatic and manual evaluation by reducing errors in
agreement. Our approach thus eliminates inflection errors, without introducing
new errors or decreasing the overall quality of the translation.
