SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
This addresses the problem of handling spatial queries for database users, but it is incremental as it adapts existing NLI approaches to a specific domain.
The paper tackles the challenge of applying natural language interfaces to databases in the spatial domain by proposing SpatialNLI, which uses a spatial comprehension model to inject spatial semantics into questions, resulting in improved translation accuracy that outperforms state-of-the-art methods.
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard to apply in the spatial domain due to the idiosyncrasy and expressiveness of the spatial questions. Inspired by the machine comprehension model, we propose a spatial comprehension model that is able to recognize the meaning of spatial entities based on the semantics of the context. The spatial semantics learned from the spatial comprehension model is then injected to the natural language question to ease the burden of capturing the spatial-specific semantics. With our spatial comprehension model and information injection, our NLI for the spatial domain, named SpatialNLI, is able to capture the semantic structure of the question and translate it to the corresponding syntax of an executable query accurately. We also experimentally ascertain that SpatialNLI outperforms state-of-the-art methods.