A Question-Oriented Propagation Network for News Reading Comprehension

Abstract

Machine reading comprehension of news articles remains to be a challenging task since the lengths of its context documents are long. Such reading comprehension task usually requires document-level language understanding while state-of-the-art, pretrained question answering models can only encode sequences with a predefined length limit. In this paper, we propose a novel Question-Oriented Propagation Network (QOPN) model for such task. Specifically, our proposed QOPN first uses a context encoding module to find local question-related clues. Then, it employs a multi-step reasoning module to aggregate question-focused information for iterative reasoning. The novel design put emphasis on capturing question-related information and allow long-range information integration, which is especially beneficial for long-context reading comprehension task. Experiments on two challenging machine comprehension datasets show that the proposed QOPN significantly outperforms previous state-of-the-art models.