Modeling Target-Side Inflection in Neural Machine Translation
Aleš Tamchyna, Marion Weller-Di Marco, Alexander Fraser · 2017
NMT systems have problems with large vocabulary sizes.Byte-pair encoding (BPE) is a popular approach to solving this problem, but while BPE allows the system to generate any target-side word, it does not enable effective generalization over the rich vocabulary in morphologically rich languages with strong inflectional phenomena.We introduce a simple approach to overcome this problem by training a system to produce the lemma of a word and its morphologically rich POS tag, which is then followed by a deterministic generation step.We apply this strategy for English-Czech and English-German translation scenarios, obtaining improvements in both settings.We furthermore show that the improvement is not due to only adding explicit morphological information.