  Question answering on knowledge bases (KBQA) poses a unique challenge for
semantic parsing research due to two intertwined challenges: large search space
and ambiguities in schema linking. Conventional ranking-based KBQA models,
which rely on a candidate enumeration step to reduce the search space, struggle
with flexibility in predicting complicated queries and have impractical running
time. In this paper, we present ArcaneQA, a novel generation-based model that
addresses both the large search space and the schema linking challenges in a
unified framework with two mutually boosting ingredients: dynamic program
induction for tackling the large search space and dynamic contextualized
encoding for schema linking. Experimental results on multiple popular KBQA
datasets demonstrate the highly competitive performance of ArcaneQA in both
effectiveness and efficiency.
