# TICO-19: the Translation Initiative for Covid-19

arXiv: https://arxiv.org/abs/2007.01788 · HTML: https://mlsanity.com/paper/2007.01788/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

## Built on by (most cited first, 30 of 68)

- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Tower: An Open Multilingual Large Language Model for Translation-Related Tasks](https://mlsanity.com/paper/2402.17733/) (2024)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Hallucinations in Large Multilingual Translation Models](https://mlsanity.com/paper/2303.16104/) (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)
- [Adaptive Machine Translation with Large Language Models](https://mlsanity.com/paper/2301.13294/) (2023)
- [CroissantLLM: A Truly Bilingual French-English Language Model](https://mlsanity.com/paper/2402.00786/) (2024)
- [Steering Large Language Models for Machine Translation with Finetuning and In-Context Learning](https://mlsanity.com/paper/2310.13448/) (2023)
- [LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages](https://mlsanity.com/paper/2407.05975/) (2024)
- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [Goldfish: Monolingual Language Models for 350 Languages](https://mlsanity.com/paper/2408.10441/) (2024)
- [The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities](https://mlsanity.com/paper/2405.20089/) (2024)
- [In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation](https://mlsanity.com/paper/2408.00397/) (2024)
- [Fine-tuning Large Language Models for Adaptive Machine Translation](https://mlsanity.com/paper/2312.12740/) (2023)
- [Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data](https://mlsanity.com/paper/2105.15071/) (2021)
- [SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languages](https://mlsanity.com/paper/2210.11621/) (2022)
- [EMMA-500: Enhancing Massively Multilingual Adaptation of Large Language Models](https://mlsanity.com/paper/2409.17892/) (2024)
- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [On the Evaluation of Machine Translation for Terminology Consistency](https://mlsanity.com/paper/2106.11891/) (2021)
- [Compositional Translation: A Novel LLM-based Approach for Low-resource Machine Translation](https://mlsanity.com/paper/2503.04554/) (2025)
- [Domain-Specific Text Generation for Machine Translation](https://mlsanity.com/paper/2208.05909/) (2022)
- [AFRIDOC-MT: Document-level MT Corpus for African Languages](https://mlsanity.com/paper/2501.06374/) (2025)
- [Multilingual Contextualization of Large Language Models for Document-Level Machine Translation](https://mlsanity.com/paper/2504.12140/) (2025)
- [Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages](https://mlsanity.com/paper/2108.06598/) (2021)
- [CODET: A Benchmark for Contrastive Dialectal Evaluation of Machine Translation](https://mlsanity.com/paper/2305.17267/) (2023)
- [EuroLLM-22B: Technical Report](https://mlsanity.com/paper/2602.05879/) (2026)
- [As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation](https://mlsanity.com/paper/2107.08357/) (2021)

## Abstract (arXiv)

The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced, "pivot" languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.