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--- |
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license: odc-by |
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task_categories: |
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- translation |
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language: |
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- en |
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- si |
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size_categories: |
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- 10K<n<100K |
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--- |
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### Licensing Information |
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The dataset is released under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this, you are also bound to the respective Terms of Use and License of the original source. |
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### Citation Information |
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``` |
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@inproceedings{ranathunga-etal-2024-quality, |
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title = "Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora", |
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author = "Ranathunga, Surangika and |
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De Silva, Nisansa and |
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Menan, Velayuthan and |
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Fernando, Aloka and |
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Rathnayake, Charitha", |
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editor = "Graham, Yvette and |
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Purver, Matthew", |
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booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)", |
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month = mar, |
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year = "2024", |
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address = "St. Julian{'}s, Malta", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.eacl-long.52", |
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pages = "860--880", |
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abstract = "We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are significant quality differences between different portions of web-mined corpora and that the quality varies across languages and datasets. We also show that, for some web-mined datasets, Neural Machine Translation (NMT) models trained with their highest-ranked 25k portion can be on par with human-curated datasets.", |
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} |
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``` |
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### Acknowledgement |
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This work was funded by the Google Award for Inclusion Research (AIR) 2022 received by Surangika Ranathunga and Nisansa de Silva. |
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We thank the NLLB Meta AI team for open sourcing the dataset. We also thank the AllenNLP team at AI2 for hosting and releasing the original NLLB dataset. |
