RikiNet: Reading Wikipedia Pages for Natural Question Answering.

Abstract

Reading long documents to answer open-domain questions remains challenging in nat-ural language understanding. In this paper, weintroduce a new model, called RikiNet, whichreads Wikipedia pages for natural question an-swering. RikiNet contains a dynamic para-graph dual-attention reader and a multi-levelcascaded answer predictor. The reader dynam-ically represents the document and questionby utilizing a set of complementary attentionmechanisms. The representations are then fedinto the predictor to obtain the span of the shortanswer, the paragraph of the long answer, andthe answer type in a cascaded manner. Onthe Natural Questions (NQ) dataset, a singleRikiNet achieves 74.3 F1 and 57.9 F1 on long-answer and short-answer tasks. To our bestknowledge, it is the first single model that out-performs the single human performance. Fur-thermore, an ensemble RikiNet obtains 76.1F1 and 61.3 F1 on long-answer and short-answer tasks, achieving the best performanceon the official NQ leaderboard