# Marian: Cost-effective High-Quality Neural Machine Translation in C++

arXiv: https://arxiv.org/abs/1805.12096 · HTML: https://mlsanity.com/paper/1805.12096/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2018.

## Built on by (most cited first, 22 of 22)

- [Understanding and Overcoming the Challenges of Efficient Transformer Quantization](https://mlsanity.com/paper/2109.12948/) (2021)
- [Outlier-Efficient Hopfield Layers for Large Transformer-Based Models](https://mlsanity.com/paper/2404.03828/) (2024)
- [Escaping the sentence-level paradigm in machine translation](https://mlsanity.com/paper/2304.12959/) (2023)
- [Neural Approaches to Multilingual Information Retrieval](https://mlsanity.com/paper/2209.01335/) (2022)
- [Modelling Latent Translations for Cross-Lingual Transfer](https://mlsanity.com/paper/2107.11353/) (2021)
- [Scaling Low-Resource MT via Synthetic Data Generation with LLMs](https://mlsanity.com/paper/2505.14423/) (2025)
- [Translation-Enhanced Multilingual Text-to-Image Generation](https://mlsanity.com/paper/2305.19216/) (2023)
- [Few-shot learning through contextual data augmentation](https://mlsanity.com/paper/2103.16911/) (2021)
- [Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning](https://mlsanity.com/paper/2312.07250/) (2023)
- [Non-Autoregressive Neural Machine Translation: A Call for Clarity](https://mlsanity.com/paper/2205.10577/) (2022)
- [Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Models](https://mlsanity.com/paper/2210.15184/) (2022)
- [The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation](https://mlsanity.com/paper/2101.00421/) (2021)
- [The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation](https://mlsanity.com/paper/2205.06618/) (2022)
- [Sockeye 3: Fast Neural Machine Translation with PyTorch](https://mlsanity.com/paper/2207.05851/) (2022)
- [SOTASTREAM: A Streaming Approach to Machine Translation Training](https://mlsanity.com/paper/2308.07489/) (2023)
- [Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models](https://mlsanity.com/paper/2210.13086/) (2022)
- [PyMarian: Fast Neural Machine Translation and Evaluation in Python](https://mlsanity.com/paper/2408.11853/) (2024)
- [Reducing Gender Bias in Machine Translation through Counterfactual Data Generation](https://mlsanity.com/paper/2311.16362/) (2023)
- [Every Bit Counts: A Theoretical Study of Precision-Expressivity Tradeoffs in Quantized Transformers](https://mlsanity.com/paper/2602.02707/) (2026)
- [Leveraging Neural Machine Translation for Word Alignment](https://mlsanity.com/paper/2103.17250/) (2021)
- [Grammatical Error Generation Based on Translated Fragments](https://mlsanity.com/paper/2104.09933/) (2021)
- [MoVoC: Morphology-Aware Subword Construction for Geez Script Languages](https://mlsanity.com/paper/2509.08812/) (2025)

## Abstract (arXiv)

This paper describes the submissions of the "Marian" team to the WNMT 2018 shared task. We investigate combinations of teacher-student training, low-precision matrix products, auto-tuning and other methods to optimize the Transformer model on GPU and CPU. By further integrating these methods with the new averaging attention networks, a recently introduced faster Transformer variant, we create a number of high-quality, high-performance models on the GPU and CPU, dominating the Pareto frontier for this shared task.