2Phuong5Nam4/vietnamese-squad-gg-translated

Vietnamese SQuAD Dataset

Dataset Description

Overview

This dataset is a Vietnamese translation of the Stanford Question Answering Dataset (SQuAD), a reading comprehension dataset consisting of questions posed on a set of Wikipedia articles. The answer to every question is a segment of text from the corresponding reading passage.

Dataset Creation

Source Data

The dataset was translated from the original English SQuAD dataset to Vietnamese. The original SQuAD is a widely-used reading comprehension benchmark dataset developed by Stanford University.

Translation Process

The dataset was created by translating the original SQuAD entries to Vietnamese while preserving the question-answer pairs and their context.

Considerations for Using the Data

Citation Information

If you use this dataset, please cite both the original SQuAD dataset and this Vietnamese version:

For the original SQuAD:

@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav  and
      Zhang, Jian  and
      Lopyrev, Konstantin  and
      Liang, Percy",
    editor = "Su, Jian  and
      Duh, Kevin  and
      Carreras, Xavier",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392",
    eprint={1606.05250},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
}

Additional Information

Dataset Curators

This Vietnamese translated version of SQuAD was prepared by phuongnam and contributors.

Licensing Information

The dataset follows the same licensing as the original SQuAD dataset, which is available under CC BY-SA 4.0.

Contributions

This dataset was created to support Vietnamese language question answering systems. Contributions to improve the dataset are welcome.