  Morphologically rich languages pose difficulties to machine translation.
Machine translation engines that rely on statistical learning from parallel
training data, such as state-of-the-art neural systems, face challenges
especially with rich morphology on the output language side. Key challenges of
rich target-side morphology in data-driven machine translation include: (1) A
large amount of differently inflected word surface forms entails a larger
vocabulary and thus data sparsity. (2) Some inflected forms of infrequent terms
typically do not appear in the training corpus, which makes closed-vocabulary
systems unable to generate these unobserved variants. (3) Linguistic agreement
requires the system to correctly match the grammatical categories between
inflected word forms in the output sentence, both in terms of target-side
morpho-syntactic wellformedness and semantic adequacy with respect to the
input.
  In this paper, we re-investigate two target-side linguistic processing
techniques: a lemma-tag strategy and a linguistically informed word
segmentation strategy. Our experiments are conducted on a English-German
translation task under three training corpus conditions of different
magnitudes. We find that a stronger Transformer baseline leaves less room for
improvement than a shallow-RNN encoder-decoder model when translating
in-domain. However, we find that linguistic modeling of target-side morphology
does benefit the Transformer model when the same system is applied to
out-of-domain input text. We also successfully apply our approach to English to
Czech translation.
