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.

论文提出了一种模块化框架，可以用来在神经机器翻译中灵活地应用语言学知识和不同类型的神经机器翻译模型，特别是设计了用于领域适应和低资源机器翻译设置的英德医疗和英立新闻测试套件，结果表明，我们的基于规则的屈折模块比神经模块更准确地引入了引文限制，并且在低成本的训练下优于现有的端到端方法。