NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages
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Abstract (arXiv)
At the center of the underlying issues that halt Indonesian natural language processing (NLP) research advancement, we find data scarcity. Resources in Indonesian languages, especially the local ones, are extremely scarce and underrepresented. Many Indonesian researchers do not publish their dataset. Furthermore, the few public datasets that we have are scattered across different platforms, thus makes performing reproducible and data-centric research in Indonesian NLP even more arduous. Rising to this challenge, we initiate the first Indonesian NLP crowdsourcing effort, NusaCrowd. NusaCrowd strives to provide the largest datasheets aggregation with standardized data loading for NLP tasks in all Indonesian languages. By enabling open and centralized access to Indonesian NLP resources, we hope NusaCrowd can tackle the data scarcity problem hindering NLP progress in Indonesia and bring NLP practitioners to move towards collaboration.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 21 Jul 2022.
Builds on
13 of 20 references.
References · 13
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- 2020IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understandingcited by 108
- 2022One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesiacited by 65
- 2021IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generationcited by 57
- 2020Masakhane -- Machine Translation For Africacited by 48
- 2021IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initializationcited by 25
- 2021IndoNLI: A Natural Language Inference Dataset for Indonesiancited by 24
- 2021Masader: Metadata Sourcing for Arabic Text and Speech Data Resourcescited by 20
- 2021Evaluating the Efficacy of Summarization Evaluation across Languagescited by 14
- 2022Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resourcescited by 11
- 2022ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Paircited by 6
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2 papers.