Accurate terminology translation is crucial for ensuring the practicality and
reliability of neural machine translation (NMT) systems. To address this,
lexically constrained NMT explores various methods to ensure pre-specified
words and phrases appear in the translation output. However, in many cases,
those methods are studied on general domain corpora, where the terms are mostly
uni- and bi-grams (>98%). In this paper, we instead tackle a more challenging
setup consisting of domain-specific corpora with much longer n-gram and highly
specialized terms. Inspired by the recent success of masked span prediction
models, we propose a simple and effective training strategy that achieves
consistent improvements on both terminology and sentence-level translation for
three domain-specific corpora in two language pairs.

本文提出了一种简单有效的训练策略，通过应用掩蔽跨度预测模型， 实现了对两种语言的三个特定领域语料库在术语级和句子级翻译方面的持续改进，以解决神经机器翻译系统术语翻译的实用性和可靠性问题。