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TICO-19: the Translation Initiative for COvid-19

Summary

The TICO-19 project addresses a communication gap during the COVID-19 pandemic. Authoritative health guidance existed mostly in major languages, so speakers of many low-resource languages were left exposed to misinformation. A coalition of academic groups, technology companies, language service providers and Translators without Borders built a shared multilingual resource. They gathered COVID-19 texts from varied public sources, including scientific (PubMed), encyclopedic (Wikipedia) and news material. Professional translators rendered the same content into every target language under strict quality review. As a result, any two languages in the set can be paired for MT development or testing.

The release has three parts:

  • a benchmark for medical-domain MT;
  • translation memories (TMX) and terminology glossaries for translators;
  • supporting monolingual and parallel data.

Baseline experiments with available MT systems show a sharp split. Well-resourced languages are translated reasonably well. Many targeted languages get very poor output, and some have no usable systems or data at all. The work matters because it gives researchers a ready testbed for crisis translation. It also gives localizers immediately usable assets for future emergencies.

Key points

  • The data covers 35 languages: 9 high-resource "pivot" languages and 26 lesser-resourced ones, mainly from Africa, South Asia and South-East Asia. Languages were chosen based on requests from relief organizations.
  • Partners include Translators without Borders, CMU, JHU, George Mason University, Amazon, Appen, Facebook, Google, Microsoft and Translated. Resources are freely available at tico-19.github.io.
  • Baseline BLEU scores were 25–49 for Spanish, French, Portuguese, Russian, Chinese and Indonesian. Lingala, Luganda, Marathi and Urdu scored below 10, which the authors consider unusable in practice.
  • The authors were unable to locate any pretrained systems or publicly available parallel data for several languages: Myanmar, Dinka, Pashto, Oromo, Nigerian Fulfulde, Dari, Nuer, Kurdish Sorani, isiZulu and Tigrinya.
  • Scores were highest on Wikipedia and news. PubMed was harder: for English→French, the gap between Wikipedia and PubMed exceeded 14 BLEU. Suggested next steps include domain adaptation, terminology-aware NMT, massively multilingual models and corpus filtering with mBERT or XLM-R.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cited by

87 papers of our library cite it (of the 140884 whose bibliographies we read); 8 of them have a page here, 79 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.nlpcovid19-2.5

@inproceedings{anastasopoulos2020tico,
  title = {{TICO-19: the Translation Initiative for COvid-19}},
  author = {Anastasopoulos, Antonios and Cattelan, Alessandro and Dou, Zi-Yi and Federico, Marcello and Federmann, Christian and Genzel, Dmitriy and Guzmán, Francisco and Hu, Junjie and Hughes, Macduff and Koehn, Philipp and Lazar, Rosie and Lewis, Will and Neubig, Graham and Niu, Mengmeng and Öktem, Alp and Paquin, Eric and Tang, Grace and Tur, Sylwia},
  booktitle = {Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020},
  year = {2020},
  doi = {10.18653/v1/2020.nlpcovid19-2.5},
  url = {https://doi.org/10.18653/v1/2020.nlpcovid19-2.5}
}

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