Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting
Nikolay Bogoychev, Pinzhen Chen · 2023
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 translatethen-refine approach which can be domainindependent and requires minimal manual efforts.We annotate random source words with pseudo-terminology translations obtained from word alignment to first train a terminologyaware model.Further, we explore two postprocessing 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.