GAMETRAPP project in progress: Designing a gamified environment for post-editing research abstracts (2024)

Abstract: The «App for post-editing neural machine translation using gamification» (GAMETRAPP) project (TED2021-129789B-I00), funded by the Spanish Ministry of Science and Innovation (2022–2024), has been in progress for a year. Thus, this paper presents its main goals and the analysis of neural machine translation and post-editing errors of research abstracts carried out. This leads to the designing of the gamified environment, which is currently under construction.

## introduction the era of artificial intelligence has undoubtedly shaped the evolution and refinement of language technologies, such as neural machine translation (nmt) systems, leading to the widespread adoption of post-editing (pe). previous studies have explored the application of pe by scholars @xcite @xcite from a first language (l1) to a second language (l2) within academic contexts. against this background of scientific dissemination, the gametrapp project aims to create a web application to bring closer the full pe of research abstracts following the iamrac structure, 1 in the iberian @xmath0, i.e., from a l1 to a l2. the potential users will be spanish nonprofessional translators, specifically scholars. ## the gametrapp project setup after a year of its inception, the gametrapp project is currently pursuing the following goals: ## nmt and pe analysis of research abstracts to design the content of the gamified environment, four linguistic tasks have been carried out. first, the compilation of research abstracts that have been selected from 244 spanish journals from the q1 and q2 in scimago journal & country rank in 2022. all abstracts have been manually analyzed and labelled considering their parts. only 126 abstracts followed these three criteria: 1) published in 2023, 2) followed the iamrac structure, and 3) written by scholars affiliated with spanish universities. second, google translate was chosen as the nmt system since it is widely used by scholars. third, the selected abstracts were both human-translated into english by a professional translator having english as l1. they were also post-edited into english by a professional post-editor having english as l1. last, the fourth task consists of the linguistic analysis of the 126 machine-translated and post-edited research abstracts, using human translation as a gold standard. to carry out this task, the gametrapp team in charge first focused on the resulting machine translation of the abstracts marking in bold red the nmt error, and then classified it based on the mqm (multidimensional quality metrics) 2 errors typology (i.e., terminology, accuracy, linguistic conventions, style, locale conventions, audience appropriateness, design and markup). next, they focused on the postediting of the abstracts marking in bold red the pe error and/or modifications in the segment under review. once the pe error is identified, they assessed the pe based on the categories proposed by the post-edit me! project foot_0 (i.e., value adding/successful edits, unnecessary edits, incomplete edits, or unsuccessful/error-introducing/missing edits). finally, they added any key issues from the evaluation that they deemed pertinent. this double analysis will not only help to determine several of the most frequent nmt errors for machine-translated research abstracts, but also to develop specific @xmath1. ## the gamified environment after carrying out the analysis, the gamified environment will be designed based on both the iamrac ----------------------- 2 available online at https://themqm.org/error-types-2/typology/ . the gamified activities are designed to train in pe through three stages: 1) the identification of nmt errors, i.e., those of google translate nmt system, 2) the practice of pe strategies in context, and 3) the pinpointing of pe errors. these activities are planned to be multiple choice (carried out by analyzing fuzzy matches different pe versions), gap-filling, and/or error correction. lastly, a questionnaire will be created within the gamified activities to collect users' experiences, and thus test the usefulness of this technique. thanks to the gametrapp gamified environment, users should have learned full pe notions of research abstracts in the iberian @xmath2.