融合了问句释义和词级别注意力的关系检测模型

在知识库问答系统任务中,由于自然语言表达方式的多样性与复杂性,语义相同表达方式不同的问句得到的答案可能不同,生成问句释义可以缓解这一问题。其次,关系检测是知识库问答系统中至关重要的一步,问答系统回答问题的准确性主要受这一步骤的影响,传统的基于注意力机制的关系检测模型没有考虑到答案路径不同抽象级别的不同重要程度。因此,本文提出了基于问句释义和词级别注意力机制的关系检测模型,用于知识库问答系统任务中,实验表明本文模型回答问题准确率较高。

Incorporating Paraphrase and Word-level Attention for Relation Detection

In the knowledge base question answer system,due to the diversity and complexity of natural language expression,the question with the same semantic but different expressions may yield different answer.The generation of paraphrase can alleviate this problem.Secondly,relation detection is a crucial step in the knowledge base question answer system.The accuracy of the question answering system to answer questions is mainly affected by this step.The traditional attention-based relation detection model does not take into account the importance of different part of the different abstract levels of the answer path expression.Therefore,this paper proposes a relation detection model based on paraphrase and word-level attention mechanism,which is used in the knowledge base question answer system end task.Experiments show that the model has higher accuracy in answering questions.