Pre-reading Activity over Question for Machine Reading Comprehension

Machine reading comprehension (MRC) is a challenging task, which has the long-standing goal of developing models that can determine the answer based on the given passage and question. Recent works have achieved promising results; however, most of them lose sight of the multiplicate information carried by the question. Inspired by the hot issue of research in the educational field, we propose a novel machine pre-reading activity over questions considering an efficient reading comprehension process of humans. Concretely, in the proposed pre-reading activity, the machine first pre-reads the question text and distinguishes the search term of the question using a question-answer type extraction algorithm (QAE). Second, the MRC model leverages two new search and enhancement methods to point out the most informative contents contained in the question, and highlight the question-based information in the passage accordingly. Third, armed with valid information, we introduce a question-based feature shunt module before the multi-head predictor. Moreover, we propose a latent A/B answer type, which not only makes the feature shunt more accurate but also enlarges the range of answer types. Experiments show that our strategies and their combinations achieve considerable improvements compared with existing methods.