  Current approaches to incorporating terminology constraints in machine
translation (MT) typically assume that the constraint terms are provided in
their correct morphological forms. This limits their application to real-world
scenarios where constraint terms are provided as lemmas. In this paper, we
introduce a modular framework for incorporating lemma constraints in neural MT
(NMT) in which linguistic knowledge and diverse types of NMT models can be
flexibly applied. It is based on a novel cross-lingual inflection module that
inflects the target lemma constraints based on the source context. We explore
linguistically motivated rule-based and data-driven neural-based inflection
modules and design English-German health and English-Lithuanian news test
suites to evaluate them in domain adaptation and low-resource MT settings.
Results show that our rule-based inflection module helps NMT models incorporate
lemma constraints more accurately than a neural module and outperforms the
existing end-to-end approach with lower training costs.
