Bridging the “gApp”: improving neural machine translation systems for multiword expression detection

Abstract

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.

Optimization of English Machine Translation by Deep Neural Network under Artificial Intelligence

“…At present, MT has been widely used in the fields of medical treatment, law, etc. In order to cope with the different language environments of different users, MT not only meets the translation needs of products, but also has the characteristics of short translation cycle [ 8 ]. Neural machine translation (NMT) gradually becomes an important research field in MT [ 9 ], thanks to its advanced translation performance.…”