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.

本研究旨在解决 “自然问题” 基准集所提出的机器阅读理解新挑战，通过提出名为 “Reflection Net” 的新方法，并采用两步训练过程来识别无答案和错误答案案例，该方法在长答案和短答案排行榜上，分别取得了 77.2 和 64.1 的 F1 得分，并经过大量实验证实其有效性。