# Many-to-English Machine Translation Tools, Data, and Pretrained Models

arXiv: https://arxiv.org/abs/2104.00290 · HTML: https://mlsanity.com/paper/2104.00290/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 59 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation](https://mlsanity.com/paper/2004.11867/) (2020)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT](https://mlsanity.com/paper/2010.06354/) (2020)
- [When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?](https://mlsanity.com/paper/1804.06323/) (2018)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [Rapid Adaptation of Neural Machine Translation to New Languages](https://mlsanity.com/paper/1808.04189/) (2018)
- [The IIT Bombay English-Hindi Parallel Corpus](https://mlsanity.com/paper/1710.02855/) (2017)
- [Balancing Training for Multilingual Neural Machine Translation](https://mlsanity.com/paper/2004.06748/) (2020)
- [PMIndia -- A Collection of Parallel Corpora of Languages of India](https://mlsanity.com/paper/2001.09907/) (2020)
- [Joey NMT: A Minimalist NMT Toolkit for Novices](https://mlsanity.com/paper/1907.12484/) (2019)
- [Nematus: a Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1703.04357/) (2017)
- [XNMT: The eXtensible Neural Machine Translation Toolkit](https://mlsanity.com/paper/1803.00188/) (2018)
- [A Universal Parent Model for Low-Resource Neural Machine Translation Transfer](https://mlsanity.com/paper/1909.06516/) (2019)

## Built on by (most cited first, 30 of 31)

- [SIB-200: A Simple, Inclusive, and Big Evaluation Dataset for Topic Classification in 200+ Languages and Dialects](https://mlsanity.com/paper/2309.07445/) (2023)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [An Open Dataset and Model for Language Identification](https://mlsanity.com/paper/2305.13820/) (2023)
- [GlotLID: Language Identification for Low-Resource Languages](https://mlsanity.com/paper/2310.16248/) (2023)
- [ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages](https://mlsanity.com/paper/2305.10510/) (2023)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [URIEL+: Enhancing Linguistic Inclusion and Usability in a Typological and Multilingual Knowledge Base](https://mlsanity.com/paper/2409.18472/) (2024)
- [ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models](https://mlsanity.com/paper/2406.09334/) (2024)
- [EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models](https://mlsanity.com/paper/2409.17892/) (2024)
- [Low-Resource Machine Translation through Retrieval-Augmented LLM Prompting: A Study on the Mambai Language](https://mlsanity.com/paper/2404.04809/) (2024)
- [Graphemic Normalization of the Perso-Arabic Script](https://mlsanity.com/paper/2210.12273/) (2022)
- [Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving Machine Translation](https://mlsanity.com/paper/2405.11937/) (2024)
- [Ibom NLP: A Step Toward Inclusive Natural Language Processing for Nigeria's Minority Languages](https://mlsanity.com/paper/2511.06531/) (2025)
- [Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search](https://mlsanity.com/paper/2305.10561/) (2023)
- [Multilingual Bidirectional Unsupervised Translation Through Multilingual Finetuning and Back-Translation](https://mlsanity.com/paper/2209.02821/) (2022)
- [Checks and Strategies for Enabling Code-Switched Machine Translation](https://mlsanity.com/paper/2210.05096/) (2022)
- [SOTASTREAM: A Streaming Approach to Machine Translation Training](https://mlsanity.com/paper/2308.07489/) (2023)
- [Generating Gender Alternatives in Machine Translation](https://mlsanity.com/paper/2407.20438/) (2024)
- [Token-level Ensembling of Models with Different Vocabularies](https://mlsanity.com/paper/2502.21265/) (2025)
- [Low-resource Machine Translation for Code-switched Kazakh-Russian Language Pair](https://mlsanity.com/paper/2503.20007/) (2025)
- [What Do Indonesians Really Need from Language Technology? A Nationwide Survey](https://mlsanity.com/paper/2506.07506/) (2025)
- [Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes](https://mlsanity.com/paper/2211.05015/) (2022)
- [OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models](https://mlsanity.com/paper/2311.14838/) (2023)
- [FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposes](https://mlsanity.com/paper/2405.11942/) (2024)
- [AfriSUD: A Dependency Treebank Collection for Evaluating Models on African Languages](https://mlsanity.com/paper/2606.12708/) (2026)
- [Sunflower: A New Approach To Expanding Coverage of African Languages in Large Language Models](https://mlsanity.com/paper/2510.07203/) (2025)
- [Segmentation Beyond Defaults: Asymmetrical Byte Pair Encoding for Optimal Machine Translation Performance](https://mlsanity.com/paper/2511.03383/) (2025)
- [YoNER: A New Yor`ub'a Multi-domain Named Entity Recognition Dataset](https://mlsanity.com/paper/2604.05624/) (2026)
- [Analysis of Numerical Localisation in LLM Translations](https://mlsanity.com/paper/2608.05232/) (2026)

## Abstract (arXiv)

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.