# NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages

arXiv: https://arxiv.org/abs/2207.10524 · HTML: https://mlsanity.com/paper/2207.10524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 21 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://mlsanity.com/paper/2009.05387/) (2020)
- [One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesia](https://mlsanity.com/paper/2203.13357/) (2022)
- [IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation](https://mlsanity.com/paper/2104.08200/) (2021)
- [Masakhane -- Machine Translation For Africa](https://mlsanity.com/paper/2003.11529/) (2020)
- [IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization](https://mlsanity.com/paper/2109.04607/) (2021)
- [IndoNLI: A Natural Language Inference Dataset for Indonesian](https://mlsanity.com/paper/2110.14566/) (2021)
- [Masader: Metadata Sourcing for Arabic Text and Speech Data Resources](https://mlsanity.com/paper/2110.06744/) (2021)
- [Evaluating the Efficacy of Summarization Evaluation across Languages](https://mlsanity.com/paper/2106.01478/) (2021)
- [Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources](https://mlsanity.com/paper/2201.10066/) (2022)
- [ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair](https://mlsanity.com/paper/2205.04651/) (2022)
- [Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19](https://mlsanity.com/paper/2005.06012/) (2020)

## Built on by (most cited first, 2 of 2)

- [GlobalBench: A Benchmark for Global Progress in Natural Language Processing](https://mlsanity.com/paper/2305.14716/) (2023)
- [Inaccessible Neural Language Models Could Reinvigorate Linguistic Nativism](https://mlsanity.com/paper/2301.05272/) (2023)

## 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.