摘要
问题生成是无监督常识问答模型的一个核心子任务,目前的方法主要是根据给定知识生成问题和答案,并为每个问题随机生成多个干扰项,然而这些方法存在干扰项与问题相关性不强且随机性较大的问题.该文提出一种面向干扰项增强的无监督常识问答模型,首先根据知识三元组生成问题和正确答案,再为问题建立对应的问题子图,得到与问题相关的三元组集合,使用注意力机制增强特征并根据问题和正确答案确定干扰项,最后使用生成的数据对模型进行训练.该模型在四个不同类型的测试任务上的结果表明,该模型优于目前的最新方法,证明了该模型的有效性.
Abstract
In contrast to the popular issue of question generation in unsupervised commonsense question-answering,this paper proposes an unsupervised commonsense question-answering model via distractor options enhancement.In our method,questions and correct answers are first generated according to the knowledge triples.Then the corre-sponding question subgraph is established for each question to obtain the knowledge triples related to the question.The attention mechanism is used to decide the distractors according to the questions and correct answers.Finally,the generated data with enhanced distractors are used to train the question-answering model.The experiment results show the model is superior to the latest methods on four different types of tasks.
基金项目
国家社会科学基金(17XXW005)
重庆理工大学研究生创新项目(clgycx203115)
维普
万方数据