Baca README ini dalam Bahasa Indonesia.
Indonesian NLP is underrepresented in research community, and one of the reasons is the lack of access to public datasets (Aji et al., 2022). To address this issue, we initiate NusaCrowd, a joint collaboration to collect NLP datasets for Indonesian languages. Help us collect and centralize Indonesian NLP datasets, and be a co-author of our upcoming paper.
You can contribute by proposing unregistered NLP dataset on our record. You can also propose datasets from your past work that have not been released to the public. Just fill out this form, and we will check and approve your entry.
We will give contribution points based on several factors, including: dataset quality, language scarcity, or task scarcity.
You can submit multiple entries, and if the total contribution points is already above the threshold, we will include you as a co-author (Generally it is enough to only propose 1-2 datasets). Read the full method of calculating points here.
Yes! Aside from new dataset collection, we are also centralizing existing datasets in a single schema that makes it easier for researchers to use Indonesian NLP datasets. You can help us there by building dataset loader. More details about that here.
The license for a dataset is not always obvious. Here are some strategies to try in your search,
- check for files such as README or LICENSE that may be distributed with the dataset itself
- check the dataset webpage
- check publications that announce the release of the dataset
- check the website of the organization providing the dataset
If no official license is listed anywhere, but you find a webpage that describes general data usage policies for the dataset, you can fall back to providing that URL in the _LICENSE variable. If you can't find any license information, please note in your PR and put _LICENSE="Unknown" in your dataset script.
You can upload your dataset publicly first, eg. on Github.
Yes, you can ask for helps in NusaCrowd's community channel! Please join our WhatsApp group or Slack server.
We greatly appreciate your help!
The artifacts of this hackathon will be described in a forthcoming academic paper targeting a machine learning or NLP audience. Please refer to this section for your contribution rewards for helping Nusantara NLP. We recognize that some datasets require more effort than others, so please reach out if you have questions. Our goal is to be inclusive with credit!