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Translation of Medical Texts using Neural Networks

机译：使用神经网络翻译医学文本

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摘要

The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. A comparison and implementation of a medical translator is the main focus of our experiments.
机译：机器翻译的质量正在迅速发展。今天，尽管系统还不完善，但人们可以在网上找到一些提供合理翻译的机器翻译系统。在某些特定领域，质量可能会下降。最近提出的针对该领域的方法是神经机器翻译。它旨在建立一个联合调谐的单神经网络，以最大化翻译性能，这是与传统统计机器翻译完全不同的方法。最近提出的神经机器翻译模型通常属于编码器-解码器系列，其中源语句被编码成固定长度的向量，然后将其解码以生成翻译。本研究考察了不同培训方法对用于医学数据的波兰英语机器翻译系统的影响。欧洲药品管理局的平行文本语料库被用作训练基于神经和统计网络的翻译系统的基础。医学翻译人员的比较和实现是我们实验的重点。

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