@inproceedings{wadhwa-etal-2018-towards,
    title = "Towards Inference-Oriented Reading Comprehension: {P}arallel{QA}",
    author = "Wadhwa, Soumya  and
      Embar, Varsha  and
      Grabmair, Matthias  and
      Nyberg, Eric",
    editor = "Bisk, Yonatan  and
      Levy, Omer  and
      Yatskar, Mark",
    booktitle = "Proceedings of the Workshop on Generalization in the Age of Deep Learning",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-1001",
    doi = "10.18653/v1/W18-1001",
    pages = "1--7",
    abstract = "In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.",
}