一种依需聚合的语义解析图查询模型

In this paper, we design and propose SemtoSql+, a semantic deep network query model based on demand aggregation.At the same time, it is a network to address the complex and cross-domain Text-to-SQL generation task.Based on LSTM and Word2Vec embedding technology, the corpus is trained as the input word vector of the model.Combined with the dependency graph method, the problem of SQL statement generation transforms into slot filling.SemtoSql+divides complex tasks into four levels and constructs by the need of aggregation, using the attention mechanism to effectively avoid the order problem in the traditional model and using a random masked mechanism to enhance the model.