@inproceedings{saikh-etal-2020-scholarlyread,
    title = "{S}cholarly{R}ead: A New Dataset for Scientific Article Reading Comprehension",
    author = "Saikh, Tanik  and
      Ekbal, Asif  and
      Bhattacharyya, Pushpak",
    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
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.lrec-1.675/",
    pages = "5498--5504",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "We present ScholarlyRead, span-of-word-based scholarly articles' Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31{\%}. The framework would be useful for building Question-Answering (QA) systems on scientific articles."
}