taidng/UIT-ViQuAD2.0

Vietnamese Question Answering Dataset

Dataset Card for UIT-ViQuAD2.0

Dataset Summary

The HF version for Vietnamese QA dataset created by Nguyen et al. (2020) and released in the shared task.

The original UIT-ViQuAD contains over 23,000 QA pairs based on 174 Vietnamese Wikipedia articles. UIT-ViQuAD2.0 adds over 12K unanswerable questions for the same passage.

The dataset has been processed to remove a few duplicated questions and answers.

Version 2.0 contains the fields is_impossible and plausible, which the authors explained in the shared task announcement:

Context: Khác với nhiều ngôn ngữ Ấn-Âu khác, tiếng Anh đã gần như loại bỏ hệ thống biến tố dựa trên cách để thay bằng cấu trúc phân tích. Đại từ nhân xưng duy trì hệ thống cách hoàn chỉnh hơn những lớp từ khác. Tiếng Anh có bảy lớp từ chính: động từ, danh từ, tính từ, trạng từ, hạn định từ (tức mạo từ), giới từ, và liên từ. Có thể tách đại từ khỏi danh từ, và thêm vào thán từ.

question: Tiếng Anh có bao nhiêu loại từ?
is_impossible: False. // There exists an answer to the question.
answer: bảy.

question: Ngôn ngữ Ấn-Âu có bao nhiêu loại từ?
is_impossible: True. // There are no correct answers extracted from the Context.
plausible_answer: bảy. // A plausible but incorrect answer extracted from the Context has the same type which the question aims to.

Specific questions about the test set or the dataset should be directed to the authors.

Languages

Vietnamese (vi)

Dataset Creation

Source Data

Vietnamese Wikipedia

Annotations

Human annotators

Citation Information

Original dataset:

@inproceedings{nguyen-etal-2020-vietnamese,
    title = "A {V}ietnamese Dataset for Evaluating Machine Reading Comprehension",
    author = "Nguyen, Kiet  and
      Nguyen, Vu  and
      Nguyen, Anh  and
      Nguyen, Ngan",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://aclanthology.org/2020.coling-main.233",
    doi = "10.18653/v1/2020.coling-main.233",
    pages = "2595--2605",
    abstract = "Over 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding a document or text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods in English and Chinese as the first experimental models on UIT-ViQuAD, which will be compared to further models. We also estimate human performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best model performances on the dataset indicate that improvements can be explored on UIT-ViQuAD through future research. Our dataset is freely available to encourage the research community to overcome challenges in Vietnamese MRC."}

Shared task where version 2.0 was published:

@article{Nguyen_2022,
   title={VLSP 2021-ViMRC Challenge: Vietnamese Machine Reading Comprehension},
   volume={38},
   ISSN={2615-9260},
   url={http://dx.doi.org/10.25073/2588-1086/vnucsce.340},
   DOI={10.25073/2588-1086/vnucsce.340},
   number={2},
   journal={VNU Journal of Science: Computer Science and Communication Engineering},
   publisher={Vietnam National University Journal of Science},
   author={Nguyen, Kiet and Tran, Son Quoc and Nguyen, Luan Thanh and Huynh, Tin Van and Luu, Son Thanh and Nguyen, Ngan Luu-Thuy},
   year={2022},
   month=dec}

Acknowledgements

We thank the authors of ViQuAD and VLSP for releasing this dataset to the community.