  Semantic parsing solves knowledge base (KB) question answering (KBQA) by
composing a KB query, which generally involves node extraction (NE) and graph
composition (GC) to detect and connect related nodes in a query. Despite the
strong causal effects between NE and GC, previous works fail to directly model
such causalities in their pipeline, hindering the learning of subtask
correlations. Also, the sequence-generation process for GC in previous works
induces ambiguity and exposure bias, which further harms accuracy. In this
work, we formalize semantic parsing into two stages. In the first stage (graph
structure generation), we propose a causal-enhanced table-filler to overcome
the issues in sequence-modelling and to learn the internal causalities. In the
second stage (relation extraction), an efficient beam-search algorithm is
presented to scale complex queries on large-scale KBs. Experiments on LC-QuAD
1.0 indicate that our method surpasses previous state-of-the-arts by a large
margin (17%) while remaining time and space efficiency. The code and models are
available at https://github.com/AOZMH/Crake.
