Datasets:

laurievb
/

open-lid-dataset

@@ -300,8 +300,64 @@ Our work aims to broaden natural language processing coverage by allowing practi
 In addition, errors in language identification can have a significant impact on downstream performance, particularly (as is often the case) when a system is used as a `black box'. The performance of our classifier is not equal across languages which could lead to worse downstream performance for particular groups. We mitigate this by providing metrics by class.
 ### Licensing Information
 ### Citation Information
-### Contributions

 In addition, errors in language identification can have a significant impact on downstream performance, particularly (as is often the case) when a system is used as a `black box'. The performance of our classifier is not equal across languages which could lead to worse downstream performance for particular groups. We mitigate this by providing metrics by class.
+## Additional information
+The dataset was curated from the sources listed below by Laurie Burchell and Nikolay Bogoychev.
 ### Licensing Information
+License considerations for each source are given below. Open use for non-commercial purposes is covered by all licences.
+If you view any part of this dataset as a violation of intellectual property rights, please let us know and we will remove it.
+| Source | Description | License |
+|---|---|---|
+|[Arabic Dialects Dataset](https://www.lancaster.ac.uk/staff/elhaj/corpora.html)| Dataset of Arabic dialects for Gulf, Egyptian, Levantine, and Tunisian Arabic dialects plus MSA|No explicit license; website describes data as "some free and useful Arabic corpora that I have created for researchers working on Arabic Natural Language Processing, Corpus and Computational Linguistics."|
+|[BLTR](https://github.com/shashwatup9k/bho-resources)|Monolingual Bhojpuri corpus|[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
+|[Global Voices](https://opus.nlpl.eu/GlobalVoices-v2015.php)|A parallel corpus of news stories from the web site Global Voices|The website for [Global Voices](https://globalvoices.org/) is licensed as [Creative Commons Attribution 3.0](https://creativecommons.org/licenses/by/3.0/). There is no explicit additional license accompanying the dataset.|
+|[Guaraní Parallel Set](https://github.com/sgongora27/giossa-gongora-guarani-2021)|Parallel Guaraní-Spanish news corpus sourced from Paraguyan websites|No explicit license|
+|[HKCanCor](https://github.com/fcbond/hkcancor)|Transcribed conversations in Hong Kong Cantonese|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode)|
+|[IADD](https://github.com/JihadZa/IADD)|Arabic dialect identification dataset covering 5 regions (Maghrebi, Levantine, Egypt, Iraq, and Gulf) and 9 countries (Algeria, Morocco, Tunisia, Palestine, Jordan, Syria, Lebanon, Egypt and Iraq). It is created from five corpora: [DART](http://qufaculty.qu.edu.qa/telsay), [SHAMI](https://github.com/GU-CLASP/shami-corpus), [TSAC](https://github.com/fbougares/TSAC), [PADIC](https://sourceforge.net/projects/padic/), and [AOC](https://www.cs.jhu.edu/data-archive/AOC-2010/). | Multiple licenses: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0) (SHAMI); [GNU Lesser General Public License v3.0](https://github.com/fbougares/TSAC/blob/master/LICENSE) (TSAC); [GNU General Public License v3](https://www.gnu.org/licenses/gpl-3.0.en.html) (PADIC). DART and AOC had no explicit license.|
+|[Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download)|A collection of corpora in different languages with an identical format.|The [Terms of Usage](https://wortschatz.uni-leipzig.de/en/usage) states "Permission for use is granted free of charge solely for non-commercial personal and scientific purposes licensed under the [Creative Commons License CC BY-NC](https://creativecommons.org/licenses/by-nc/4.0/)."|
+|[LTI](https://www.cs.cmu.edu/~ralf/langid.html)|Training data for language identification|From the README: "With the exception of the contents of the Europarl/, ProjectGutenberg/, and PublicDomain/ directories, all code and text in this corpus are copyrighted. However, they may be redistributed under the terms of various Creative Commons licenses and the GNU GPL.  Copying the unmodified archive noncommercially is permitted by all of the licenses. For commercial redistribution or redistribution of modified versions, please consult the individual licenses."|
+|[MADAR Shared Task 2019, subtask 1](https://camel.abudhabi.nyu.edu/madar-shared-task-2019/)|Dialectal Arabic in the travel domain|The MADAR Corpus has a custom license, the text of which can be found in this repo.|
+|[EM corpus](http://lepage-lab.ips.waseda.ac.jp/en/projects/meiteilon-manipuri-language-resources/)|Parallel Manipuri-English sentences crawled from [The Sangai Express](https://www.thesangaiexpress.com/)|[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)|
+|[MIZAN](https://github.com/omidkashefi/Mizan)|Parallel Persian-English corpus from literature domain|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)|
+|[MT560 v1](https://opus.nlpl.eu/MT560.php)|A machine translation dataset for over 500 languages to English. We have filtered out data from OPUS-100, Europarl, Open Subtitles, Paracrawl, Wikimedia, Wikimatrix, Wikititles, and Common Crawl due to issues with the fidelity of the language labels. |[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)|
+|[NLLB Seed](https://github.com/facebookresearch/flores/blob/main/nllb_seed/README.md)|Around 6000 sentences in 39 languages sampled from Wikipedia, intended to cover languages lacking training data.|[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)|
+|[SETIMES](https://opus.nlpl.eu/SETIMES.php)|A parallel corpus of news articles in the Balkan languages|[CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/)|
+|[Tatoeba](https://opus.nlpl.eu/Tatoeba.php)|Collaborative sentence translations|[CC BY 2.0 FR](https://creativecommons.org/licenses/by/2.0/fr/)|
+|[Tehran English-Persian parallel corpus (TEP)](https://opus.nlpl.eu/TEP.php)|Parallel Persian-English sentences sourced from subtitles|[GNU General Public License](https://www.gnu.org/licenses/gpl-3.0.html)|
+|[Turkic Interlingua (TIL) Corpus](https://github.com/turkic-interlingua/til-mt)|A large-scale parallel corpus combining most of the public datasets for 22 Turkic languages|[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
+|[WiLI-2018](https://zenodo.org/record/841984)|Wikipedia language identification benchmark containing 235K paragraphs of 235 languages|[Open Data Commons Open Database License (ODbL) v1.0](https://opendatacommons.org/licenses/odbl/1-0/)|
+|[XL-Sum](https://github.com/csebuetnlp/xl-sum)|Summarisation dataset covering 44 languages, sourced from BBC News|[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)|
 ### Citation Information
+If you use this dataset, please cite all the authors [in the citation file](https://github.com/laurieburchell/open-lid-dataset/blob/main/citations.bib) who compiled the source datasets, plus the OpenLID paper:
+```bibtex
+@inproceedings{burchell-etal-2023-open,
+    title = "An Open Dataset and Model for Language Identification",
+    author = "Burchell, Laurie  and
+      Birch, Alexandra  and
+      Bogoychev, Nikolay  and
+      Heafield, Kenneth",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-short.75",
+    doi = "10.18653/v1/2023.acl-short.75",
+    pages = "865--879",
+    abstract = "Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033{\%} across 201 languages, outperforming previous work. We achieve this by training on a curated dataset of monolingual data, which we audit manually to ensure reliability. We make both the model and the dataset available to the research community. Finally, we carry out detailed analysis into our model{'}s performance, both in comparison to existing open models and by language class.",
+}
+```
+### Contributions
+Thanks to @hac541309 and @davanstrien for adding this dataset.