GAMETRAPP: Training app for post-editing neural machine trans-lation using gamification in professional settings

2928

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nan

Abstract

AbstractThe GAMETRAPP project, funded by Spanish Ministry for Science and Inno-vation, aims to facilitate industry profes-sionals training on full post-editing of neural machine translation by means of a gamified environment.

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© 2023 The authors. This article is licensed under a Creative Commons 4.0 licence, no derivative works, attribution, CC-BY-ND. GAMETRAPP: Training app for post-editing neural machine trans- lation using gamification in professional settings Cristina Toledo-Báez Research Institute on Multilingual Language Technologies University of Málaga Spain toledo@uma.es Abstract The GAMETRAPP project, funded by the Spanish Ministry for Science and Innova- tion, aims to facilitate professionals from technical fields training on full post-edit- ing of neural machine translation by means of a gamified environment. 1 Introduction Digital transition refers to the actual effect of digitization —the technical conversion of analog information into digital form— and digitalization —the actual process of change in industries— on society (Chaume Varela, 2019). Human language technologies are of paramount importance in this process because the digital transition is incom- plete until it is multilingual. In this context, machine translation, and mainly Neural Machine Translation (NMT), is gaining ground since it helps to meet the communicative needs of an increasingly demanding digital soci- ety. The real potential and correct use of NMT is only achieved through professional post-editing (PE) by human translators and/or post-editors. However, considering the multilingual needs im- posed by digital transition, especially in the tech- nical and technological domains, demand for NMT plus full PE carried out in part by non-pro- fessional translators will increase exponentially in the coming years. Consequently, initiatives for training non-professional translators on NMT plus full PE are expected to be demanded in the near future. Previous studies have explored professional en- gineers’ (Temizöz, 2013) or academics’ (Parra Escartín & Goulet, 2020) performance as post-ed- itors. However, apart from MultitraiNMT (For- cada et al., 2022), designed for language learners, as far as the author is aware, no proposal has yet been made in the training of non-professional translators on full PE. To fill that gap, the main contribution of GAM- ETRAPP project is to bring training in NMT and full PE closer to professionals from technical fields using an innovative training approach: gam- ification. Based on the application of play ele- ments affecting motivation and knowledge appre- hension (Toukoumidis & Maeöts, 2019), gamifi- cation is nowadays widely used in enterprises to motivate employees’ involvement in the company as well as in corporate and lifelong learning train- ing (Iacono et al., 2020). 2 Project description The GAMETRAPP project is funded by the Spanish Ministry for Science and Innovation (TED2021-129789B-I00). It started in December 2022, and it will last for two years. The GAMETRAPP team is an international and inter-university group formed by 19 researchers from 9 Universities, 7 from Spain (University of Málaga, University of Córdoba, University Pablo Olavide, University of Alcalá, University Autónoma de Madrid, University of Valladolid and Valencia International University) and 2 from United States (Kent State University and Utah Valley University). In addition, an outsourced company will help design, develop, and create the gamified environment. The main hypothesis is that in the English– Spanish language combination, gamification can help professionals from technical fields having a high English proficiency …

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Labeled Text

AbstractThe GAMETRAPP project, funded by Spanish Ministry for Science and Inno-vation, aims to facilitate industry profes-sionals training on full post-editing of neural machine translation by means of a gamified environment.

Publication Details

Paper ID:
2928
Year:
2023
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan