breakend/nllb-multi-domain

Dataset Card for NLLB Multi-Domain

Dataset Summary

NLLB Multi Domain is a set of professionally-translated sentences in News, Unscripted informal speech, and Health domains. It is designed to enable assessment of out-of-domain performance and to study domain adaptation for machine translation. Each domain has approximately 3000 sentences.

Supported Tasks and Leaderboards

Multilingual Machine Translation

Refer to the Dynabench leaderboard for additional details on model evaluation on FLORES-101 in the context of the WMT2021 shared task on Large-Scale Multilingual Machine Translation. Flores 200 is an extention of this.

Languages

Language	FLORES-200 code
Central Aymara	ayr_Latn
Bhojpuri	bho_Deva
Dyula	dyu_Latn
Friulian	fur_Latn
Russian	rus_Cyrl
Wolof	wol_Latn

Use a hyphenated pairing to get two langauges in one datapoint (e.g., "eng_Latn-rus_Cyrl" will provide sentences in the format below).

Dataset Structure

Data Instances

See Dataset Viewer.

The text is provided as-in the original dataset, without further preprocessing or tokenization.

Data Fields

• id: Row number for the data entry, starting at 1.
• sentence: The full sentence in the specific language (may have _lang for pairings)
• domain: The domain of the sentence.

Dataset Creation

Please refer to the original article No Language Left Behind: Scaling Human-Centered Machine Translation for additional information on dataset creation.

Additional Information

Dataset Curators

See paper for details.

Licensing Information

Licensed with Creative Commons Attribution Share Alike 4.0. License available here.

Citation Information

Please cite the authors if you use these corpora in your work:

@article{nllb2022,
  author    = {NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi,  Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers, Safiyyah Saleem, Holger Schwenk, Jeff Wang},
  title     = {No Language Left Behind: Scaling Human-Centered Machine Translation},
  year      = {2022}
}

Please also cite prior work that this dataset builds on:

@inproceedings{,
  title={The FLORES-101  Evaluation Benchmark for Low-Resource and Multilingual Machine Translation},
  author={Goyal, Naman and Gao, Cynthia and Chaudhary, Vishrav and Chen, Peng-Jen and Wenzek, Guillaume and Ju, Da and Krishnan, Sanjana and Ranzato, Marc'Aurelio and Guzm\'{a}n, Francisco and Fan, Angela},
  year={2021}
}

@inproceedings{,
  title={Two New Evaluation Datasets for Low-Resource Machine Translation: Nepali-English and Sinhala-English},
  author={Guzm\'{a}n, Francisco and Chen, Peng-Jen and Ott, Myle and Pino, Juan and Lample, Guillaume and Koehn, Philipp and Chaudhary, Vishrav and Ranzato, Marc'Aurelio},
  journal={arXiv preprint arXiv:1902.01382},
  year={2019}
}