论文标题
Rikinet：阅读Wikipedia页面以回答自然问题
RikiNet: Reading Wikipedia Pages for Natural Question Answering
论文作者
论文摘要
在自然语言理解中阅读长期文档以回答开放域问题仍然具有挑战性。在本文中，我们介绍了一种名为Rikinet的新模型，该模型读取Wikipedia页面以进行自然问题回答。 Rikinet包含动态段落的双重注意读者和一个多级级联的答案预测指标。读者通过利用一组互补的注意机制动态地表示文档和问题。然后将表示形式馈入预测因子中，以获得短答案的跨度，长答案的段落以及答案类型以级联的方式类型。关于自然问题（NQ）数据集，单个Rikinet在长期和短路任务上实现了74.3 F1和57.9 F1。据我们所知，这是第一个胜过单一人类表现的单一模型。此外，合奏Rikinet在长期和短距离任务上获得了76.1 F1和61.3 F1，在NQ官方排行榜上取得了最佳表现
Reading long documents to answer open-domain questions remains challenging in natural language understanding. In this paper, we introduce a new model, called RikiNet, which reads Wikipedia pages for natural question answering. RikiNet contains a dynamic paragraph dual-attention reader and a multi-level cascaded answer predictor. The reader dynamically represents the document and question by utilizing a set of complementary attention mechanisms. The representations are then fed into the predictor to obtain the span of the short answer, the paragraph of the long answer, and the answer type in a cascaded manner. On the Natural Questions (NQ) dataset, a single RikiNet achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks. To our best knowledge, it is the first single model that outperforms the single human performance. Furthermore, an ensemble RikiNet obtains 76.1 F1 and 61.3 F1 on long-answer and short-answer tasks, achieving the best performance on the official NQ leaderboard
