融合事实文本的问句分解式语义解析方法

Incorporating Fact Text Semantic Parsing Via Question Decomposition

目前知识库问答(Knowledge base question answering,KBQA)技术无法有效地处理复杂问题,难以理解其中的复杂语义.将一个复杂问题先分解再整合,是解析复杂语义的有效方法.但是,在问题分解的过程中往往会出现实体判断错误或主题实体缺失的情况,导致分解得到的子问题与原始复杂问题并不匹配.针对上述问题,提出了一种融合事实文本的问解分解式语义解析方法.对复杂问题的处理分为分解-抽取-解析3个阶段,首先把复杂问题分解成简单子问题,然后抽取问句中的关键信息,最后生成结构化查询语句.同时,本文又构造了事实文本库,将三元组转化成用自然语言描述的句子,采用注意力机制获取更丰富的知识.在ComplexWebQuestions数据集上的实验表明,本文提出的模型在性能上优于其他基线模型.

Recently,knowledge base question answering(KBQA)technology cannot effectively deal with complex questions because it is difficult to understand complex semantics.For a complex question,first decomposing and then integrating is an effective method to parse complex semantics.However,in the process of question decomposition,there are often cases of misjudged entities or missing subject entities.As a result,the decomposed sub-questions do not match the original complex questions.To address the above problems,this paper proposes a decomposed semantic parsing method that incorporating fact texts.The processing of complex questions is divided into three stages:decomposition-extraction-parsing.First,decompose the complex questions into simple sub-questions,then extract the key information in the question,and finally generate the structured query.At the same time,this paper constructs the fact text database,transforms the triples into sentences described by natural language,and adopts the attention mechanism to obtain more abundant knowledge.Experiments on the ComplexWebQuestions dataset show that the proposed model outperforms other baseline models.