@inproceedings{kabir-etal-2024-make,
    title = "You Make me Feel like a Natural Question: Training {QA} Systems on Transformed Trivia Questions",
    author = "Kabir, Tasnim  and
      Sung, Yoo Yeon  and
      Bandyopadhyay, Saptarashmi  and
      Zou, Hao  and
      Chandra, Abhranil  and
      Boyd-Graber, Jordan Lee",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.emnlp-main.1140/",
    doi = "10.18653/v1/2024.emnlp-main.1140",
    pages = "20486--20510",
    abstract = "Training question-answering QA and information retrieval systems for web queries require large, expensive datasets that are difficult to annotate and time-consuming to gather. Moreover, while natural datasets of information-seeking questions are often prone to ambiguity or ill-formed, there are troves of freely available, carefully crafted question datasets for many languages. Thus, we automatically generate shorter, information-seeking questions, resembling web queries in the style of the Natural Questions (NQ) dataset from longer trivia data. Training a QA system on these transformed questions is a viable strategy for alternating to more expensive training setups showing the F1 score difference of less than six points and contrasting the final systems."
}