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Building neural machine translation systems for multilingual participatory spaces

Lohar, Pintu orcid logoORCID: 0000-0002-5328-1585, Xie, Guodong orcid logoORCID: 0000-0003-0037-8495, Gallagher, Daniel and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2023) Building neural machine translation systems for multilingual participatory spaces. Analytics, 2 (2). pp. 393-409. ISSN 2813-2203

Abstract
This work presents the development of the translation component in a multistage, multilevel, multimode, multilingual and dynamic deliberative (M4D2) system, built to facilitate automated moderation and translation in the languages of five European countries: Italy, Ireland, Germany, France and Poland. Two main topics were to be addressed in the deliberation process: (i) the environment and climate change; and (ii) the economy and inequality. In this work, we describe the development of neural machine translation (NMT) models for these domains for six European languages: Italian, English (included as the second official language of Ireland), Irish, German, French and Polish. As a result, we generate 30 NMT models, initially baseline systems built using freely available online data, which are then adapted to the domains of interest in the project by (i) filtering the corpora, (ii) tuning the systems with automatically extracted in-domain development datasets and (iii) using corpus concatenation techniques to expand the amount of data available. We compare our results produced by the domain-adapted systems with those produced by Google Translate, and demonstrate that fast, high-quality systems can be produced that facilitate multilingual deliberation in a secure environment.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:neural machine translation; domain adaptation; parallel data; deliberative democracy; citizens’ assemblies
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Publisher:MDPI
Official URL:https://doi.org/10.3390/analytics2020022
Copyright Information:© 2023 The Authors.
Funders:European Commission under H2020-EU.3.6.—SOCIETAL CHALLENGES—Europe In A Changing World—Inclusive, Innovative And Reflective Societies, grant agreement ID: 959234, Science Foundation Ireland Grant Agreement No. 13/RC/2106_P2 at the ADAPT SFI Research Centre at Dublin City University
ID Code:28311
Deposited On:05 May 2023 16:23 by Pintu Lohar . Last Modified 05 May 2023 16:25
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