论文标题
没有答案比错误的答案更好：文档级别机器阅读理解的反射模型
No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension
论文作者
论文摘要
自然问题（NQ）基准设置为机器阅读理解带来了新的挑战：答案不仅处于不同级别的粒度（长和短），而且还具有更丰富的类型（包括无答案，是/否，单跨和多跨度）。在本文中，我们针对此挑战，并系统地处理所有答案类型。特别是，我们提出了一种称为反射网的新颖方法，该方法利用了两步训练程序来识别No-Asswer和错误的解雇案例。进行了广泛的实验来验证我们方法的有效性。在纸质撰写时（5月〜20，〜2020），我们的方法在长答案排行榜上获得了前1位，F1分别为77.2和64.1。
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.
