Translation into a morphologically rich language requires a large output vocabulary to model various morphological phenomena, which is a challenge for neural machine translation architectures. To address this issue, the present paper investigates the impact of having two output factors with a system able to generate separately two distinct representations of the target words. Within this framework, we investigate several word representations that correspond to different distributions of morpho-syntactic information across both factors. We report experiments for translation from English into two morphologically rich languages, Czech and Latvian, and show the importance of explicitly modeling target morphology.
Similarly, REF investigated the impact of different word representation models in the context of factored NMT.
Word Representations in Factored Neural Machine Translation
Proceedings of the Second Conference on Machine Translation
Proceedings of the Second Conference on Machine Translation
Computer Science