To solve the problem of translation of professional vocabulary in the biomedical field and help biological researchers to translate and understand foreign language documents, we proposed a semantic disambiguation model and external dictionaries to build a novel translation model for biomedical texts based on the transformer model. The proposed biomedical neural machine translation system (BioNMT) adopts the sequence-to-sequence translation framework, which is based on deep neural networks. To construct the specialized vocabulary of biology and medicine, a hybrid corpus was obtained using a crawler system extracting from universal corpus and biomedical corpus. The experimental results showed that BioNMT which composed by professional biological dictionary and Transformer model increased the bilingual evaluation understudy (BLEU) value by 14.14%, and the perplexity was reduced by 40%. And compared with Google Translation System and Baidu Translation System, BioNMT achieved better translations about paragraphs and resolve the ambiguity of biomedical name entities to greatly improved.

中文翻译：

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BioNMT：生物医学神经机器翻译系统

为了解决生物医学领域专业词汇的翻译问题，并帮助生物学研究人员翻译和理解外文文献，我们提出了语义歧义消除模型和外部词典，以基于转换模型的方式为生物医学文本建立了新颖的翻译模型。所提出的生物医学神经机器翻译系统（BioNMT）采用了基于深度神经网络的序列到序列翻译框架。为了构建生物学和医学专业词汇，使用从通用语料库和生物医学语料库中提取的爬虫系统获得了混合语料库。实验结果表明，由专业生物词典和Transformer模型组成的BioNMT使双语评估学习（BLEU）值提高了14.14％，困惑度降低了40％。与Google翻译系统和百度翻译系统相比，BioNMT在段落方面实现了更好的翻译，并解决了生物医学名称实体的歧义性大大提高的问题。