  Machine translation systems are vulnerable to domain mismatch, especially
when the task is low-resource. In this setting, out of domain translations are
often of poor quality and prone to hallucinations, due to the translation model
preferring to predict common words it has seen during training, as opposed to
the more uncommon ones from a different domain. We present two simple methods
for improving translation quality in this particular setting: First, we use
lexical shortlisting in order to restrict the neural network predictions by IBM
model computed alignments. Second, we perform $n$-best list reordering by
reranking all translations based on the amount they overlap with each other.
Our methods are computationally simpler and faster than alternative approaches,
and show a moderate success on low-resource settings with explicit out of
domain test sets. However, our methods lose their effectiveness when the domain
mismatch is too great, or in high resource setting.
