@inproceedings{le-etal-2022-vimqa,
    title = "{VIMQA}: A {V}ietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering",
    author = "Le, Khang  and
      Nguyen, Hien  and
      Le Thanh, Tung  and
      Nguyen, Minh",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.lrec-1.700/",
    pages = "6521--6529",
    abstract = "Vietnamese is the native language of over 98 million people in the world. However, existing Vietnamese Question Answering (QA) datasets do not explore the model{'}s ability to perform advanced reasoning and provide evidence to explain the answer. We introduce VIMQA, a new Vietnamese dataset with over 10,000 Wikipedia-based multi-hop question-answer pairs. The dataset is human-generated and has four main features: (1) The questions require advanced reasoning over multiple paragraphs. (2) Sentence-level supporting facts are provided, enabling the QA model to reason and explain the answer. (3) The dataset offers various types of reasoning to test the model{'}s ability to reason and extract relevant proof. (4) The dataset is in Vietnamese, a low-resource language. We also conduct experiments on our dataset using state-of-the-art Multilingual single-hop and multi-hop QA methods. The results suggest that our dataset is challenging for existing methods, and there is room for improvement in Vietnamese QA systems. In addition, we propose a general process for data creation and publish a framework for creating multilingual multi-hop QA datasets. The dataset and framework are publicly available to encourage further research in Vietnamese QA systems."
}