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

Authors :
Lankford, Séamus
Afli, Haithem
Way, Andy
Source :
Language Resources & Evaluation. Dec2023, Vol. 57 Issue 4, p1671-1696. 26p.
Publication Year :
2023

Abstract

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 kgCO 2 emissions generated during model development. The application is freely available (http://github.com/adaptNMT). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1574020X
Volume :
57
Issue :
4
Database :
Academic Search Index
Journal :
Language Resources & Evaluation
Publication Type :
Academic Journal
Accession number :
173723398
Full Text :
https://doi.org/10.1007/s10579-023-09671-2