The present research introduces the tool gApp , a Python-based text preprocessing system for the automatic identification and conversion of discontinuous multiword expressions (MWEs) into their continuous form in order to enhance neural machine translation (NMT). To this end, an experiment with semi-fixed verb–noun idiomatic combinations (VNICs) will be carried out in order to evaluate to what extent gApp can optimise the performance of the two main free open-source NMT systems —Google Translate and DeepL— under the challenge of MWE discontinuity in the Spanish into English directionality. In the light of our promising results, the study concludes with suggestions on how to further optimise MWE-aware NMT systems.

中文翻译：

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桥接“ gApp”：改进用于多词表达检测的神经机器翻译系统

本研究介绍了工具gApp，这是一个基于Python的文本预处理系统，用于自动识别不连续的多单词表达式（MWE）并将其转换为连续形式，以增强神经机器翻译（NMT）。为此，将进行半固定动词-名词惯用语组合（VNIC）的实验，以评估gApp可以在多大程度上优化两个主要的免费开源NMT系统（Google Translate和DeepL）的性能。在MWE不连续性的挑战下，西班牙语变成了英语方向性。根据我们令人鼓舞的结果，该研究最后提出了有关如何进一步优化支持MWE的NMT系统的建议。