论文标题
域适应的多语言神经机器翻译的影响
Impact of Domain-Adapted Multilingual Neural Machine Translation in the Medical Domain
论文作者
论文摘要
多语言神经机器翻译（MNMT）模型在培训过程中利用许多语言对，通过从高资源语言中转移知识来提高低资源语言的翻译质量。我们研究了具有自动指标的英国罗马尼亚人的医疗领域中域适应的MNMT模型的质量，以及包括特定于术语的错误类别的人为错误类型学注释。我们将室外MNMT与适应的MNMT进行比较。内域MNMT模型在所有测量的自动指标中的表现都优于室外MNMT，并且产生较少的术语错误。
Multilingual Neural Machine Translation (MNMT) models leverage many language pairs during training to improve translation quality for low-resource languages by transferring knowledge from high-resource languages. We study the quality of a domain-adapted MNMT model in the medical domain for English-Romanian with automatic metrics and a human error typology annotation which includes terminology-specific error categories. We compare the out-of-domain MNMT with the in-domain adapted MNMT. The in-domain MNMT model outperforms the out-of-domain MNMT in all measured automatic metrics and produces fewer terminology errors.
