  Out-of-vocabulary word translation is a major problem for the translation of
low-resource languages that suffer from a lack of parallel training data. This
paper evaluates the contributions of target-language context models towards the
translation of OOV words, specifically in those cases where OOV translations
are derived from external knowledge sources, such as dictionaries. We develop
both neural and non-neural context models and evaluate them within both
phrase-based and self-attention based neural machine translation systems. Our
results show that neural language models that integrate additional context
beyond the current sentence are the most effective in disambiguating possible
OOV word translations. We present an efficient second-pass lattice-rescoring
method for wide-context neural language models and demonstrate performance
improvements over state-of-the-art self-attention based neural MT systems in
five out of six low-resource language pairs.
