Design of an Open-Source Architecture for Neural Machine Translation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 6 Mar 2024.
Builds on
16 of 35 references.
References · 16
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2018A Call for Clarity in Reporting BLEU Scorescited by 1,790
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,290
- 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
- 2018Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyondcited by 434
- 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
- 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
- 2018Marian: Fast Neural Machine Translation in C++cited by 202
- 2018Quality expectations of machine translationcited by 9
4 more
- 2024Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021cited by 9
- 2024Transformers for Low-Resource Languages: Is F'eidir Linn!cited by 9
- 2024Human Evaluation of English--Irish Transformer-Based NMTcited by 3
- 2024adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translationcited by 2
Built on by
Not cited yet.