Producing Unseen Morphological Variants in Statistical Machine Translation
Matthias Huck, Ales Tamchyna, Ondrej Bojar, Alexander Fraser
EACL 2017

Translating into morphologically rich languages is difficult. Although
the coverage of lemmas may be reasonable, many morphological variants
cannot be learned from the training data.  We present a statistical
translation system that is able to produce these inflected word
forms. Different from most previous work, we do not separate
morphological prediction from lexical choice into two consecutive
steps.  Our approach is novel in that it is integrated in decoding and
takes advantage of context information from both the source language
and the target language sides.