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adaptNMT: an open-source, language-agnostic development environment for neural machine translation

Lankford, Seamus, Afli, Haithem orcid logoORCID: 0000-0002-7449-4707 and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2023) adaptNMT: an open-source, language-agnostic development environment for neural machine translation. Language Resources and Evaluation, 57 . pp. 1671-1696. ISSN 1574-020X

adaptNMT streamlines all processes involved in the development and deployment of RNN and Transformer neural translation models. As an open-source application, it is designed for both technical and non-technical users who work in the field of machine translation. Built upon the widely-adopted OpenNMT ecosystem, the application is particularly useful for new entrants to the field since the setup of the development environment and creation of train, validation and test splits is greatly simplified. Graphing, embedded within the application, illustrates the progress of model training, and SentencePiece is used for creating subword segmentation models. Hyperparameter customization is facilitated through an intuitive user interface, and a single-click model development approach has been implemented. Models developed by adaptNMT can be evaluated using a range of metrics, and deployed as a translation service within the application. To support eco-friendly research in the NLP space, a green report also flags the power consumption and kgCO2 emissions generated during model development. The application is freely available (http://github.com/adaptNMT).
Item Type:Article (Published)
Uncontrolled Keywords:Neural machine translation; Language technology; NMT; Natural language processing; Green NLP
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Computer engineering
Computer Science > Information technology
Computer Science > Interactive computer systems
Computer Science > Machine learning
Computer Science > Machine translating
Humanities > Language
Humanities > Translating and interpreting
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Official URL:https://doi.org/10.1007/s10579-023-09671-2
Copyright Information:© 2023 The Authors
Funders:Science Foundation Ireland through ADAPT Centre (Grant No. 13/RC/2106), Munster Technological University, National Relay Station (NRS) of Ireland., Open Access funding provided by the IReL Consortium.
ID Code:28791
Deposited On:20 Jul 2023 12:21 by Andrew Way . Last Modified 21 Nov 2023 12:26

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