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            <title>Proceedings of the 1st Workshop on Open Community-Driven Machine Translation</title>
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            <namePart type="given">Miquel</namePart>
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    <abstract>adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is built upon the widely-adopted OpenNMT ecosystem, and is particularly useful for new entrants to the field, as it simplifies the setup of the development environment and creation of train, validation, and test splits. The application offers a graphing feature that illustrates the progress of model training, and employs SentencePiece for creating subword segmentation models. Furthermore, the application provides an intuitive user interface that facilitates hyperparameter customization. Notably, a single-click model development approach has been implemented, and models developed by adaptNMT can be evaluated using a range of metrics. To encourage eco-friendly research, adaptNMT incorporates a green report that flags the power consumption and kgCO2 emissions generated during model development. The application is freely available.</abstract>
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%0 Conference Proceedings
%T Design of an Open-Source Architecture for Neural Machine Translation
%A Lankford, Séamus
%A Afli, Haithem
%A Way, Andy
%Y Esplà-Gomis, Miquel
%Y Forcada, Mikel L.
%Y Kuzman, Taja
%Y Ljubešić, Nikola
%Y van Noord, Rik
%Y Ramírez-Sánchez, Gema
%Y Tiedemann, Jörg
%Y Toral, Antonio
%S Proceedings of the 1st Workshop on Open Community-Driven Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F lankford-etal-2023-design
%X adaptNMT is an open-source application that offers a streamlined approach to the development and deployment of Recurrent Neural Networks and Transformer models. This application is built upon the widely-adopted OpenNMT ecosystem, and is particularly useful for new entrants to the field, as it simplifies the setup of the development environment and creation of train, validation, and test splits. The application offers a graphing feature that illustrates the progress of model training, and employs SentencePiece for creating subword segmentation models. Furthermore, the application provides an intuitive user interface that facilitates hyperparameter customization. Notably, a single-click model development approach has been implemented, and models developed by adaptNMT can be evaluated using a range of metrics. To encourage eco-friendly research, adaptNMT incorporates a green report that flags the power consumption and kgCO2 emissions generated during model development. The application is freely available.
%U https://aclanthology.org/2023.crowdmt-1.2
%P 15-20

Markdown (Informal)

[Design of an Open-Source Architecture for Neural Machine Translation](https://aclanthology.org/2023.crowdmt-1.2) (Lankford et al., CrowdMT 2023)

• Design of an Open-Source Architecture for Neural Machine Translation (Lankford et al., CrowdMT 2023)

ACL

• Séamus Lankford, Haithem Afli, and Andy Way. 2023. Design of an Open-Source Architecture for Neural Machine Translation. In Proceedings of the 1st Workshop on Open Community-Driven Machine Translation, pages 15–20, Tampere, Finland. European Association for Machine Translation.