In this paper we present research results with Paidiom, a text-preprocessing algorithm designed for 1) converting discontinuous multiword expressions (MWEs) into their continuous forms and 2) translemmatising them, i.e., converting source-text MWEs into their target-text equivalents, in order to improve the performance of current neural machine translation (NMT) systems. To test its effectiveness, an experiment with the NMT systems of VIP, Google Translate and DeepL has been carried out in the ES>EN translation direction with Verb-Noun Idiomatic Constructions (VNICs) in Spanish. The performance of Paidiom was compared to both the one of our previous algorithm (gApp) and to the manual conversion (our gold standard). In this regard, the promising results yielded by this study, the first one analysing Paidiom’s performance, will shed some light on new avenues for enhancing MWE-aware NMT systems.
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