  Machine translation systems are vulnerable to domain mismatch, especially in
a low-resource scenario. Out-of-domain translations are often of poor quality
and prone to hallucinations, due to exposure bias and the decoder acting as a
language model. We adopt two approaches to alleviate this problem: lexical
shortlisting restricted by IBM statistical alignments, and hypothesis
re-ranking based on similarity. The methods are computationally cheap, widely
known, but not extensively experimented on domain adaptation. We demonstrate
success on low-resource out-of-domain test sets, however, the methods are
ineffective when there is sufficient data or too great domain mismatch. This is
due to both the IBM model losing its advantage over the implicitly learned
neural alignment, and issues with subword segmentation of out-of-domain words.
