RT Conference Proceedings
T1 gApp: a text preprocessing system to improve the neural machine translation of discontinuous multiword expressions
A1 Hidalgo Ternero, Carlos Manuel
A1 Zhou Lian, Xiaoqing
K1 Traducción automática
AB 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.
YR 2022
FD 2022
LK https://hdl.handle.net/10630/25650
UL https://hdl.handle.net/10630/25650
LA eng
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026