  The semantic parsing-based method is an important research branch for
knowledge-based question answering. It usually generates executable programs
lean upon the question and then conduct them to reason answers over a knowledge
base. Benefit from this inherent mechanism, it has advantages in the
performance and the interpretability. However, traditional semantic parsing
methods usually generate a complete program before executing it, which
struggles with multi-hop question answering over heterogeneous knowledge. On
one hand, generating a complete multi-hop program relies on multiple
heterogeneous supporting facts, and it is difficult for generators to
understand these facts simultaneously. On the other hand, this way ignores the
semantic information of the intermediate answers at each hop, which is
beneficial for subsequent generation. To alleviate these challenges, we propose
a self-iterative framework for multi-hop program generation (HopPG) over
heterogeneous knowledge, which leverages the previous execution results to
retrieve supporting facts and generate subsequent programs hop by hop. We
evaluate our model on MMQA-T^2, and the experimental results show that HopPG
outperforms existing semantic-parsing-based baselines, especially on the
multi-hop questions.
