2026-06-01T07:59:17Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/256502026-02-03T11:49:43Zcom_10630_2254col_10630_37959

gApp: a text preprocessing system to improve the neural machine translation of discontinuous multiword expressions Hidalgo Ternero, Carlos Manuel Zhou Lian, Xiaoqing Traducción automática In this paper we present research results with gApp, a text-preprocessing system designed for automati-cally detecting and converting discontinuous multiword expressions (MWEs) into their continuous forms so as to improve the performance of current neural machine translation systems (NMT) (see Hidalgo-Ternero, 2021 & 2022, Hidalgo-Ternero & Corpas Pastor, 2020, 2022a & 2022b, Hidalgo-Ternero, Lista, and Corpas Pastor, 2022, and Hidalgo-Ternero and Zhou-Lian, 2022a & 2022b). To test its effectiveness, eight experiments with several NMT systems such as DeepL, Google Translate, ModernMT and VIP have been carried out in different language directionalities (ES/FR/IT > ES/EN/DE/FR/IT/PT/ZH) for the trans-lation of somatisms, i.e., MWEs containing lexemes referring to human or animal body parts (Mellado Blanco, 2004). More specifically, we have analysed both flexible verb-noun idiomatic constructions (VNICs) and flexible verb + prepositional phrase (VPP) constructions. In this regard, the promising results obtained for these typologies of MWEs throughout experiments 1-8 will shed some light on new avenues for enhancing MWE-aware NMT systems. 2022-12-20T12:28:41Z 2022-12-20T12:28:41Z 2022 conference output https://hdl.handle.net/10630/25650 eng Translating and the Computer conference — TC44 Luxemburgo, Luxemburgo 24/11/2022 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional