The 2020 WMT Biomedical translation task evaluated Medline abstract
translations. This is a small-domain translation task, meaning limited relevant
training data with very distinct style and vocabulary. Models trained on such
data are susceptible to exposure bias effects, particularly when training
sentence pairs are imperfect translations of each other. This can result in
poor behaviour during inference if the model learns to neglect the source
sentence.
The UNICAM entry addresses this problem during fine-tuning using a robust
variant on Minimum Risk Training. We contrast this approach with data-filtering
to remove `problem' training examples. Under MRT fine-tuning we obtain good
results for both directions of English-German and English-Spanish biomedical
translation. In particular we achieve the best English-to-Spanish translation
result and second-best Spanish-to-English result, despite using only single
models with no ensembling.

本研究使用最小风险训练方法对 WMT 医学翻译任务进行微调，解决训练样本不足和句子对不匹配等问题，取得了较好的翻译结果。