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Question answering on knowledge bases (KBQA) poses a unique challenge forsemantic parsing research due to two intertwined challenges: large search spaceand ambiguities in schema linking. Conventional ranking-based KBQA models,which rely on a candidate enumeration step to reduce the search space, strugglewith flexibility in predicting complicated queries and have impractical runningtime. In this paper, we present ArcaneQA, a novel generation-based model thataddresses both the large search space and the schema linking challenges in aunified framework with two mutually boosting ingredients: dynamic programinduction for tackling the large search space and dynamic contextualizedencoding for schema linking. Experimental results on multiple popular KBQAdatasets demonstrate the highly competitive performance of ArcaneQA in botheffectiveness and efficiency.

ArcaneQA: Dynamic Program Induction and Contextualized Encoding for Knowledge Base Question Answering