earticle

영어

Typical Text-to-SQL research copes with the problem of parsing natural language questions into executable SQL queries. While new datasets emerge progressively, most are committed to exploring the robustness and generalizability of models, leaving the research of model's capability in solving geography-specific questions blank. To this end, in this work, we approach this problem by proposing an improved abstract syntax tree to represent spatial SQL queries. Following this proposition, we build SpatialSpider, a new Text-to-SQL dataset in the geography domain. Our dataset introduces the following challenges: 1) Understanding of geographical semantics. 2) Prediction of spatial functions. In summary, we propose an improved abstract syntax tree and a dataset to tackle the spatial Text-to-SQL problem and obtain promising experimental results.