Machine Reading Comprehension
ZHANG Zhaobin, WANG Suge, CHEN Xin, ZHAO Linling, WANG Dian
.
2020, 34(6):
89-96,105.
Among the Chinese reading comprehension of the college entrance examination, the opinion questions are rich in abastract viewpoint expressions. In order to obtain the answer information related to the questions from the reading materials, the abstract words in the questions need to be expanded, resulting an expansion of the opinion questions. This paper proposes a question expansion modeling method with the multi-task hierarchical Long Short-Term Memory (Multi-HLSTM). First, the reading materials and the questions are connected with attention mechanism. At the same time, the two tasks of the questions prediction and the answers prediction are modeled to further expand the questions. Finally, the extended questions and the original questions are applied to extract the candidate sentences of the questions as the answers. On the data sets of opinion questions reading comprehensions of the Chinese college entrance examination, its related simulation test and the datasets of description and opinion type of DuReader, the experimental results show that the proposed question expansion model is effective on the extraction of candidate sentences.