From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser
  for Complex Question Answering over Knowledge Base
Wangzhen Guo Linyin Luo Hanjiang Lai Jian Yin
Parsing questions into executable logical forms has showed impressive results
for knowledge-base question answering (KBQA). However, complex KBQA is a more
challenging task that requires to perform complex multi-step reasoning.
Recently, a new semantic parser called KoPL has been proposed to explicitly
model the reasoning processes, which achieved the state-of-the-art on complex
KBQA. In this paper, we further explore how to unlock the reasoning ability of
semantic parsers by a simple proposed parse-execute-refine paradigm. We refine
and improve the KoPL parser by demonstrating the executed intermediate
reasoning steps to the KBQA model. We show that such simple strategy can
significantly improve the ability of complex reasoning. Specifically, we
propose three components: a parsing stage, an execution stage and a refinement
stage, to enhance the ability of complex reasoning. The parser uses the KoPL to
generate the transparent logical forms. Then, the execution stage aligns and
executes the logical forms over knowledge base to obtain intermediate reasoning
processes. Finally, the intermediate step-by-step reasoning processes are
demonstrated to the KBQA model in the refinement stage. With the explicit
reasoning processes, it is much easier to answer the complex questions.
Experiments on benchmark dataset shows that the proposed PER-KBQA performs
significantly better than the stage-of-the-art baselines on the complex KBQA.