SEACrowd/ara_close

The dataset contribution of this study is a compilation of short fictional stories written in Bikol for readability assessment. The data was combined other collected Philippine language corpora, such as Tagalog and Cebuano. The data from these languages are all distributed across the Philippine elementary system's first three grade levels (L1, L2, L3). We sourced this dataset from Let's Read Asia (LRA), Bloom Library, Department of Education, and Adarna House.

Languages

bcl, ceb

Supported Tasks

Readability Assessment

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("ara_close", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("ara_close"))
# 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://github.com/imperialite/ara-close-lang

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 Ara Close dataloader in your work, please cite the following:

@inproceedings{imperial-kochmar-2023-automatic,
    title = "Automatic Readability Assessment for Closely Related Languages",
    author = "Imperial, Joseph Marvin  and
      Kochmar, Ekaterina",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.331",
    doi = "10.18653/v1/2023.findings-acl.331",
    pages = "5371--5386",
    abstract = "In recent years, the main focus of research on automatic readability assessment (ARA)     has shifted towards using expensive deep learning-based methods with the primary goal of increasing models{'} accuracy.     This, however, is rarely applicable for low-resource languages where traditional handcrafted features are still     widely used due to the lack of existing NLP tools to extract deeper linguistic representations. In this work,     we take a step back from the technical component and focus on how linguistic aspects such as mutual intelligibility     or degree of language relatedness can improve ARA in a low-resource setting. We collect short stories written in three     languages in the Philippines{---}Tagalog, Bikol, and Cebuano{---}to train readability assessment models and explore the     interaction of data and features in various cross-lingual setups. Our results show that the inclusion of CrossNGO,     a novel specialized feature exploiting n-gram overlap applied to languages with high mutual intelligibility,     significantly improves the performance of ARA models compared to the use of off-the-shelf large multilingual     language models alone. Consequently, when both linguistic representations are combined, we achieve state-of-the-art     results for Tagalog and Cebuano, and baseline scores for ARA in Bikol.",
}


@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}
}