@inproceedings{mishra-etal-2020-reading,
    title = "Reading Comprehension as Natural Language Inference:A Semantic Analysis",
    author = "Mishra, Anshuman  and
      Patel, Dhruvesh  and
      Vijayakumar, Aparna  and
      Li, Xiang  and
      Kapanipathi, Pavan  and
      Talamadupula, Kartik",
    editor = "Gurevych, Iryna  and
      Apidianaki, Marianna  and
      Faruqui, Manaal",
    booktitle = "Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.starsem-1.2",
    pages = "12--19",
    abstract = "In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.",
}