SEACrowd/xquadr

XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive QA dataset) that is a part of the LAReQA benchmark. Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each question (out of around 1200) appears in 11 different languages and has 11 parallel correct answers across the languages. It is designed so as to include parallel QA pairs across languages, allowing questions to be matched with answers from different languages. The span-tagging task in XQuAD is converted into a retrieval task by breaking up each contextual paragraph into sentences, and treating each sentence as a possible target answer. There are around 1000 candidate answers in each language.

Languages

tha, vie

Supported Tasks

Question Answering Retrieval

Dataset Usage

Using datasets library

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

Using seacrowd library

# Load the dataset using the default config
dset = sc.load_dataset("xquadr", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("xquadr"))
# 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/google-research-datasets/lareqa

Dataset Version

Source: 1.1.0. SEACrowd: 2024.06.20.

Dataset License

Creative Commons Attribution Share Alike 4.0 (cc-by-sa-4.0)

Citation

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

@article{,@inproceedings{roy-etal-2020-lareqa,
    title = "{LAR}e{QA}: Language-Agnostic Answer Retrieval from a Multilingual Pool",
    author = "Roy, Uma  and
        Constant, Noah  and
        Al-Rfou, Rami  and
        Barua, Aditya  and
        Phillips, Aaron  and
        Yang, Yinfei",
    editor = "Webber, Bonnie  and
        Cohn, Trevor  and
        He, Yulan  and
        Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.emnlp-main.477",
    doi = "10.18653/v1/2020.emnlp-main.477",
    pages = "5919--5930",
}


@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}
}