@inproceedings{galvan-sosa-2019-active,
    title = "Active Reading Comprehension: A Dataset for Learning the Question-Answer Relationship Strategy",
    author = "Galv{\'a}n-Sosa, Diana",
    editor = "Alva-Manchego, Fernando  and
      Choi, Eunsol  and
      Khashabi, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P19-2014/",
    doi = "10.18653/v1/P19-2014",
    pages = "106--112",
    abstract = "Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader{'}s understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation."
}