@inproceedings{wang-etal-2020-answer-better,
    title = "No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension",
    author = "Wang, Xuguang  and
      Shou, Linjun  and
      Gong, Ming  and
      Duan, Nan  and
      Jiang, Daxin",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.findings-emnlp.370/",
    doi = "10.18653/v1/2020.findings-emnlp.370",
    pages = "4141--4150",
    abstract = "The Natural Questions (NQ) benchmark set brings new challenges to Machine Reading Comprehension: the answers are not only at different levels of granularity (long and short), but also of richer types (including no-answer, yes/no, single-span and multi-span). In this paper, we target at this challenge and handle all answer types systematically. In particular, we propose a novel approach called Reflection Net which leverages a two-step training procedure to identify the no-answer and wrong-answer cases. Extensive experiments are conducted to verify the effectiveness of our approach. At the time of paper writing (May. 20, 2020), our approach achieved the top 1 on both long and short answer leaderboard, with F1 scores of 77.2 and 64.1, respectively."
}