# TURJUMAN: A Public Toolkit for Neural Arabic Machine Translation

arXiv: https://arxiv.org/abs/2206.03933 · HTML: https://mlsanity.com/paper/2206.03933/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 59 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [Phrase-Based & Neural Unsupervised Machine Translation](https://mlsanity.com/paper/1804.07755/) (2018)
- [AraT5: Text-to-Text Transformers for Arabic Language Generation](https://mlsanity.com/paper/2109.12068/) (2021)
- [AraNet: A Deep Learning Toolkit for Arabic Social Media](https://mlsanity.com/paper/1912.13072/) (2019)
- [Investigating Code-Mixed Modern Standard Arabic-Egyptian to English Machine Translation](https://mlsanity.com/paper/2105.13573/) (2021)
- [QCRI Machine Translation Systems for IWSLT 16](https://mlsanity.com/paper/1701.03924/) (2017)
- [First Result on Arabic Neural Machine Translation](https://mlsanity.com/paper/1606.02680/) (2016)
- [A Recipe for Arabic-English Neural Machine Translation](https://mlsanity.com/paper/1808.06116/) (2018)
- [Simple Automatic Post-editing for Arabic-Japanese Machine Translation](https://mlsanity.com/paper/1907.06210/) (2019)

## Built on by (most cited first, 12 of 12)

- [Dolphin: A Challenging and Diverse Benchmark for Arabic NLG](https://mlsanity.com/paper/2305.14989/) (2023)
- [TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://mlsanity.com/paper/2308.03051/) (2023)
- [Toucan: Many-to-Many Translation for 150 African Language Pairs](https://mlsanity.com/paper/2407.04796/) (2024)
- [Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation](https://mlsanity.com/paper/2310.16127/) (2023)
- [Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model](https://mlsanity.com/paper/2505.17894/) (2025)
- [An Evaluation of Persian-English Machine Translation Datasets with Transformers](https://mlsanity.com/paper/2302.00321/) (2023)
- [DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers](https://mlsanity.com/paper/2405.16482/) (2024)
- [Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks?](https://mlsanity.com/paper/2507.20419/) (2025)
- [Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs](https://mlsanity.com/paper/2601.13099/) (2026)
- [AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translation](https://mlsanity.com/paper/2609.22796/) (2026)
- [SinaTools: Open Source Toolkit for Arabic Natural Language Processing](https://mlsanity.com/paper/2411.01523/) (2024)
- [MASRAD: Arabic Terminology Management Corpora with Semi-Automatic Construction](https://mlsanity.com/paper/2503.19211/) (2025)

## Abstract (arXiv)

We present TURJUMAN, a neural toolkit for translating from 20 languages into Modern Standard Arabic (MSA). TURJUMAN exploits the recently-introduced text-to-text Transformer AraT5 model, endowing it with a powerful ability to decode into Arabic. The toolkit offers the possibility of employing a number of diverse decoding methods, making it suited for acquiring paraphrases for the MSA translations as an added value. To train TURJUMAN, we sample from publicly available parallel data employing a simple semantic similarity method to ensure data quality. This allows us to prepare and release AraOPUS-20, a new machine translation benchmark. We publicly release our translation toolkit (TURJUMAN) as well as our benchmark dataset (AraOPUS-20).