Terminology-Aware Translation with Constrained Decoding and Large
  Language Model Prompting
Nikolay Bogoychev Pinzhen Chen
Terminology correctness is important in the downstream application of machine
translation, and a prevalent way to ensure this is to inject terminology
constraints into a translation system. In our submission to the WMT 2023
terminology translation task, we adopt a translate-then-refine approach which
can be domain-independent and requires minimal manual efforts. We annotate
random source words with pseudo-terminology translations obtained from word
alignment to first train a terminology-aware model. Further, we explore two
post-processing methods. First, we use an alignment process to discover whether
a terminology constraint has been violated, and if so, we re-decode with the
violating word negatively constrained. Alternatively, we leverage a large
language model to refine a hypothesis by providing it with terminology
constraints. Results show that our terminology-aware model learns to
incorporate terminologies effectively, and the large language model refinement
process can further improve terminology recall.