Papers with Marian
TranslateLocally: Blazing-fast translation running on the local CPU (2021.emnlp-demo)
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| Challenge: | Using cloud-based translation providers carries privacy risks, as users lose control of their data once it enters the web. |
| Approach: | They propose a desktop translation application that runs locally on a user's desktop or laptop CPU. translateLocally delivers cloud-like translation speed and quality even on 10 year old hardware. |
| Outcome: | The open-source translation system runs on Linux, Windows and macOS on desktops and laptops. |
Marian: Fast Neural Machine Translation in C++ (P18-4)
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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, Alexandra Birch
| Challenge: | In this paper, we present Marian, an efficient and self-contained Neural Machine Translation framework . Marian is written in pure C++ with minimal dependencies . |
| Approach: | They present Marian, an efficient and self-contained Neural Machine Translation framework written in pure C++ with minimal dependencies. |
| Outcome: | The proposed framework achieves high training and translation speed with minimal dependencies . it is currently being deployed in multiple European projects . |
PyMarian: Fast Neural Machine Translation and Evaluation in Python (2024.emnlp-demo)
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| Challenge: | a Python interface to Marian NMT is available in PyPI via pip install pymarian . the interface provides a speedup factor of up to 7.8 the existing implementations . |
| Approach: | They propose a Python interface to Marian NMT, a C++-based training and inference toolkit for sequence-to-sequence models. |
| Outcome: | The proposed interface enables models trained with Marian to be connected to Python tools with a speedup factor of up to 7.8 the existing implementations. |