SEACrowd/ac_iquad

This is an automatically-produced question answering datasetgenerated from Indonesian Wikipedia articles. Each entryin the dataset consists of a context paragraph, thequestion and answer, and the question's equivalent SPARQLquery. Questions are separated into two subsets: simple(question consists of a single SPARQL triple pattern) andcomplex (question consists of two triples plus an optionaltyping triple).

Languages

ind

Supported Tasks

Question Answering

Dataset Usage

Using datasets library

from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/ac_iquad", trust_remote_code=True)

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("ac_iquad", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("ac_iquad"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")

More details on how to load the seacrowd library can be found here.

Dataset Homepage

https://www.kaggle.com/datasets/realdeo/indonesian-qa-generated-by-kg

Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution 4.0 (cc-by-4.0)

Citation

If you are using the Ac Iquad dataloader in your work, please cite the following:

@article{afa5bf8149d6406786539c1ea827087d,
    title = "AC-IQuAD: Automatically Constructed Indonesian Question Answering Dataset by Leveraging Wikidata",
    abstract = "Constructing a question-answering dataset can be prohibitively expensive, making it difficult for researchers
    to make one for an under-resourced language, such as Indonesian. We create a novel Indonesian Question Answering dataset
    that is produced automatically end-to-end. The process uses Context Free Grammar, the Wikipedia Indonesian Corpus, and
    the concept of the proxy model. The dataset consists of 134 thousand simple questions and 60 thousand complex questions.
    It achieved competitive grammatical and model accuracy compared to the translated dataset but suffers from some issues
    due to resource constraints.",
    keywords = "Automatic dataset construction, Question answering dataset, Under-resourced Language",
    author = "Kerenza Doxolodeo and Krisnadhi, {Adila Alfa}",
    note = "Publisher Copyright: {	extcopyright} 2024, The Author(s).",
    year = "2024",
    doi = "10.1007/s10579-023-09702-y",
    language = "English",
    journal = "Language Resources and Evaluation",
    issn = "1574-020X",
    publisher = "Springer Netherlands",
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}