Linguistically-informed target-word representations for machine
translation

Linguistic generalization is a critical issue for translation to
morphologically rich languages. The talk will begin by briefly
presenting frameworks implemented using phrase-based statistical
machine translation for translation to German and Czech, with a focus
on handling rich inflection and compounding. It will then present two
approaches for use with neural machine translation, generalization
over word stems and linguistically-informed segmentation.