# Nematus: a Toolkit for Neural Machine Translation

arXiv: https://arxiv.org/abs/1703.04357 · HTML: https://mlsanity.com/paper/1703.04357/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2017.

## Built on by (most cited first, 16 of 16)

- [Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET](https://mlsanity.com/paper/2202.05148/) (2022)
- [Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model](https://mlsanity.com/paper/2401.12873/) (2024)
- [Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback](https://mlsanity.com/paper/1805.01252/) (2018)
- [Many-to-English Machine Translation Tools, Data, and Pretrained Models](https://mlsanity.com/paper/2104.00290/) (2021)
- [On the Evaluation of Commit Message Generation Models: An Experimental Study](https://mlsanity.com/paper/2107.05373/) (2021)
- [Translate, then Parse! A strong baseline for Cross-Lingual AMR Parsing](https://mlsanity.com/paper/2106.04565/) (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)
- [How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?](https://mlsanity.com/paper/2109.01100/) (2021)
- [Semantics-aware Attention Improves Neural Machine Translation](https://mlsanity.com/paper/2110.06920/) (2021)
- [Counterfactual Learning from Human Proofreading Feedback for Semantic Parsing](https://mlsanity.com/paper/1811.12239/) (2018)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [Progressive Translation: Improving Domain Robustness of Neural Machine Translation with Intermediate Sequences](https://mlsanity.com/paper/2305.09154/) (2023)
- [Enhancing the Transformer Decoder with Transition-based Syntax](https://mlsanity.com/paper/2101.12640/) (2021)
- [Exploring Supervised and Unsupervised Rewards in Machine Translation](https://mlsanity.com/paper/2102.11403/) (2021)
- [Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies](https://mlsanity.com/paper/2203.13550/) (2022)
- [Lemmatization as a Classification Task: Results from Arabic across Multiple Genres](https://mlsanity.com/paper/2506.18399/) (2025)

## Abstract (arXiv)

We present Nematus, a toolkit for Neural Machine Translation. The toolkit prioritizes high translation accuracy, usability, and extensibility. Nematus has been used to build top-performing submissions to shared translation tasks at WMT and IWSLT, and has been used to train systems for production environments.