Data to 14 Oct 2026

The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT

arXiv 2010.06354 ↗First posted 13 Oct 2020cs.CLcited by 128

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Abstract (arXiv)

This paper describes the development of a new benchmark for machine translation that provides training and test data for thousands of language pairs covering over 500 languages and tools for creating state-of-the-art translation models from that collection. The main goal is to trigger the development of open translation tools and models with a much broader coverage of the World's languages. Using the package it is possible to work on realistic low-resource scenarios avoiding artificially reduced setups that are common when demonstrating zero-shot or few-shot learning. For the first time, this package provides a comprehensive collection of diverse data sets in hundreds of languages with systematic language and script annotation and data splits to extend the narrow coverage of existing benchmarks. Together with the data release, we also provide a growing number of pre-trained baseline models for individual language pairs and selected language groups.

Key facts. Cited by 128 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Oct 2020.

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Citing papers per year: 2021: 18; 2022: 17; 2023: 22; 2024: 25; 2025: 24; 2026: 22.

128 papers.

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2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
2024Aya Model: An Instruction Finetuned Open-Access Multilingual Language Modelcited by 295
2024Tower: An Open Multilingual Large Language Model for Translation-Related Taskscited by 206
2024Large Language Models for Education: A Survey and Outlookcited by 188
2023DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Taskcited by 131
2021Survey of Low-Resource Machine Translationcited by 92
2023Inseq: An Interpretability Toolkit for Sequence Generation Modelscited by 78
2021The paradox of the compositionality of natural language: a neural machine translation case studycited by 59
2022Democratizing Neural Machine Translation with OPUS-MTcited by 58
2023Adaptive Machine Translation with Large Language Modelscited by 58
2021Understanding the Properties of Minimum Bayes Risk Decoding in Neural Machine Translationcited by 56
2024Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languagescited by 38

38 more

2022Frustratingly Easy Label Projection for Cross-lingual Transfercited by 33
2023Quantifying the Dialect Gap and its Correlates Across Languagescited by 33
2023When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languagescited by 32
2021Many-to-English Machine Translation Tools, Data, and Pretrained Modelscited by 31
2021How BPE Affects Memorization in Transformerscited by 29
2024Goldfish: Monolingual Language Models for 350 Languagescited by 29
2025Middle-Layer Representation Alignment for Cross-Lingual Transfer in Fine-Tuned LLMscited by 29
2023Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk Decodingcited by 28
2021A Large-Scale Study of Machine Translation in the Turkic Languagescited by 21
2022Rainproof: An Umbrella To Shield Text Generators From Out-Of-Distribution Datacited by 21
2024Poro 34B and the Blessing of Multilingualitycited by 18
2021Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extractioncited by 16
2022SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languagescited by 16
2024EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Modelscited by 16
2025An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)cited by 16
2022Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practicecited by 15
2023Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxificationcited by 15
2021Endangered Languages are not Low-Resourced!cited by 14
2023Angler: Helping Machine Translation Practitioners Prioritize Model Improvementscited by 12
2024Small Languages, Big Models: A Study of Continual Training on Languages of Norwaycited by 12
2025SMOL: Professionally translated parallel data for 115 under-represented languagescited by 12
2022GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentationcited by 11
2024MINERS: Multilingual Language Models as Semantic Retrieverscited by 11
2025Improving Multilingual Language Models by Aligning Representations through Steeringcited by 11
2022PreQuEL: Quality Estimation of Machine Translation Outputs in Advancecited by 9
2024Did Translation Models Get More Robust Without Anyone Even Noticing?cited by 9
2024Can LLMs get help from other LLMs without revealing private information?cited by 9
2021Evaluating Multiway Multilingual NMT in the Turkic Languagescited by 8
2021A Massively Multilingual Analysis of Cross-linguality in Shared Embedding Spacecited by 8
2022Better Quality Estimation for Low Resource Corpus Miningcited by 8
2023Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translationcited by 8
2024The Impact of Copyrighted Material on Large Language Models: A Norwegian Perspectivecited by 8
2025NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmarkcited by 8
2023Improving Machine Translation with Phrase Pair Injection and Corpus Filteringcited by 7
2021Majority Voting with Bidirectional Pre-translation For Bitext Retrievalcited by 5
2022Selective Text Augmentation with Word Roles for Low-Resource Text Classificationcited by 5
2022Too Brittle To Touch: Comparing the Stability of Quantization and Distillation Towards Developing Lightweight Low-Resource MT Modelscited by 5
2024Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrasescited by 5

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