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Parsing natural language questions into executable logical forms is a usefuland interpretable way to perform question answering on structured data such asknowledge bases (KB) or databases (DB). However, existing approaches onsemantic parsing cannot adapt to both modalities, as they suffer from theexponential growth of the logical form candidates and can hardly generalize tounseen data. In this work, we propose Uni-Parser, a unified semantic parser forquestion answering (QA) on both KB and DB. We introduce the primitive (relationand entity in KB, and table name, column name and cell value in DB) as anessential element in our framework. The number of primitives grows linearlywith the number of retrieved relations in KB and DB, preventing us from dealingwith exponential logic form candidates. We leverage the generator to predictfinal logical forms by altering and composing topranked primitives withdifferent operations (e.g. select, where, count). With sufficiently prunedsearch space by a contrastive primitive ranker, the generator is empowered tocapture the composition of primitives enhancing its generalization ability. Weachieve competitive results on multiple KB and DB QA benchmarks moreefficiently, especially in the compositional and zero-shot settings.

Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database