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 . |
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| Challenge: | Neural machine translation (NMT) has made remarkable progress over the past few years. |
| Approach: | They propose to use C++ and NVIDIA’s GPU-accelerated libraries to build an open-source neural machine translation toolkit called CytonMT. |
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From Research to Production and Back: Ludicrously Fast Neural Machine Translation (D19-56)
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Young Jin Kim, Marcin Junczys-Dowmunt, Hany Hassan, Alham Fikri Aji, Kenneth Heafield, Roman Grundkiewicz, Nikolay Bogoychev
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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 . |
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Fast and Accurate Neural Machine Translation with Translation Memory (2021.acl-long)
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| Challenge: | Existing knowledge demonstrates the superiority of TM-based neural machine translation only on TM specialized tasks . |
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| Challenge: | a majority of Asians speak low to medium resource languages . lack of resources poses a challenge, which requires innovative solutions . |
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| Challenge: | State-of-the-art neural machine translation models deliver high-quality translations at the expense of high inference latency and energy consumption. |
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The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation (D18-1)
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| Challenge: | Neural sequence-to-sequence models have proven effective for machine translation, but at the expense of interpretability. |
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Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages (2020.acl-srw)
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| Challenge: | Neural Machine Translation (NMT) is a rapidly advancing MT paradigm that can be used to improve machine translation for many languages. |
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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. |
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A Tree-based Decoder for Neural Machine Translation (D18-1)
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| Challenge: | Existing work on adding syntactic information to NMT systems is limited to linguistically-inspired tree structures. |
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