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Marian

Marian is a fast C++ NMT framework with dynamic graphs and built-in autodiff for research and deployment.

Translation Updated 13 hours ago
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Marian

Marian's Top Features

Efficient, self-contained NMT framework
Integrated automatic differentiation engine
Dynamic computation graphs
Implemented entirely in C++
Fast training and translation speed
Research-friendly and extensible design
Encoder–decoder architecture
No external machine learning frameworks required
Competitive with state-of-the-art systems
Open-source availability

Frequently asked questions about Marian

Marian is an efficient, self-contained neural machine translation (NMT) framework written entirely in C++ with an integrated automatic differentiation engine based on dynamic computation graphs.

Marian was developed by researchers including Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, Hieu Hoang, Kenneth Heafield, Tom Neckermann, Frank Seide, Ulrich Germann, Alham Fikri Aji, Nikolay Bogoychev, André F. T. Martins, and Alexandra Birch.

Marian offers fast training and translation, an integrated automatic differentiation engine using dynamic computation graphs, a C++ implementation, and a research-friendly, extensible design.

Marian is implemented entirely in C++.

Yes. Marian is research-friendly, allowing efficient implementation and testing of new ideas in neural machine translation.

No. Marian is self-contained and does not rely on external machine learning frameworks.

Marian is available on its arXiv preprint page, which provides links to the PDF, TeX source, and other formats.

Marian uses an encoder–decoder framework and dynamic computation graphs for efficient network construction and automatic differentiation.

Marian focuses on machine translation, specifically neural approaches for translating text between languages using deep learning.

Marian was first submitted to arXiv on April 1, 2018, with the current version revised on April 4, 2018.

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Category
Translation
Tags
neural machine translation
NMT
C++
automatic differentiation
dynamic computation graphs
encoder–decoder architecture
research
deployment
high performance
extensibility

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