A Survey on Semantic Parsing
It provides a comprehensive overview for researchers and practitioners, but it is incremental as it synthesizes existing work without new results.
This survey examines semantic parsing, which converts natural language to logical forms for querying knowledge bases, covering components from rule-based to neural methods and discussing supervision levels and learning challenges.
A significant amount of information in today's world is stored in structured and semi-structured knowledge bases. Efficient and simple methods to query them are essential and must not be restricted to only those who have expertise in formal query languages. The field of semantic parsing deals with converting natural language utterances to logical forms that can be easily executed on a knowledge base. In this survey, we examine the various components of a semantic parsing system and discuss prominent work ranging from the initial rule based methods to the current neural approaches to program synthesis. We also discuss methods that operate using varying levels of supervision and highlight the key challenges involved in the learning of such systems.