@inproceedings{chen-etal-2021-neuer,
    title = "{NEU}er at {S}em{E}val-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension",
    author = "Chen, Zhixiang  and
      Lei, Yikun  and
      Liu, Pai  and
      Guo, Guibing",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2021.semeval-1.110/",
    doi = "10.18653/v1/2021.semeval-1.110",
    pages = "827--832",
    abstract = "SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension. Most existing approaches propose to concat question and option together to form a context-aware model. However, we argue that straightforward concatenation can only provide a coarse-grained context for the MRC task, ignoring the specific positions of the option relative to the question. In this paper, we propose a novel MRC model by filling options into the question to produce a fine-grained context (defined as summary) which can better reveal the relationship between option and question. We conduct a series of experiments on the given dataset, and the results show that our approach outperforms other counterparts to a large extent."
}