在高考语文阅读理解中,观点类问题中的观点表达较为抽象,为了从阅读材料中获取与问题相关的答案信息,需要对问题中的抽象词语进行扩展,达到扩展观点类问题的目的。该文提出了基于多任务层级长短时记忆网络(Multi-HLSTM)的问题扩展建模方法。首先将阅读材料与问题进行交互注意,同时建模问题预测和答案预测两个任务,使模型对问题进一步扩展。最后将扩展后的问题与原问题同时应用于问题的答案候选句抽取中。通过在高考语文观点类的真题、模拟题以及DuReader的描述观点类数据集上进行实验,验证了本文的问题扩展模型对答案候选句的抽取性能具有一定的提升作用。
Abstract
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.
关键词
问题扩展 /
高考语文 /
阅读理解 /
观点类题型
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Key words
question expansion /
Chinese college entrance examination /
reading comprehension /
opinion questions
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脚注
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基金
国家重点研发计划(2018YFB1005103);国家自然科学基金(61573231);山西省重点研发计划项目(201803D421024)
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