@inproceedings{dzendzik-etal-2017-adapt,
    title = "{ADAPT} Centre Cone Team at {IJCNLP}-2017 Task 5: A Similarity-Based Logistic Regression Approach to Multi-choice Question Answering in an Examinations Shared Task",
    author = "Dzendzik, Daria  and
      Poncelas, Alberto  and
      Vogel, Carl  and
      Liu, Qun",
    editor = "Liu, Chao-Hong  and
      Nakov, Preslav  and
      Xue, Nianwen",
    booktitle = "Proceedings of the {IJCNLP} 2017, Shared Tasks",
    month = dec,
    year = "2017",
    address = "Taipei, Taiwan",
    publisher = "Asian Federation of Natural Language Processing",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/I17-4010/",
    pages = "67--72",
    abstract = "We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi-choice Question Answering in Examinations shared task. The system is based on a logistic regression over the string similarities between question, answer, and additional text. We obtain the highest grade out of six systems: 48.7{\%} accuracy on a validation set (vs. a baseline of 29.45{\%}) and 45.6{\%} on a test set."
}