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.
Firstly,a complete multi-hop program relies on multiple heterogeneous
supporting facts, and it is difficult for models to receive these facts
simultaneously. Secondly,these methods ignore the interaction information
between the previous-hop execution result and the current-hop program
generation. To alleviate these challenges, we propose a self-iterative
framework for multi-hop program generation (HopPG) over heterogeneous
knowledge, which leverages the previous-hop execution results to retrieve
supporting facts and generate subsequent programs iteratively. We evaluate our
model on MMQA-T^2. The experimental results show that HopPG outperforms
existing semantic-parsing-based baselines, especially on the multi-hop
questions.

我们提出了一个自我迭代的多跳程序生成框架（HopPG），用于异构知识上的多跳程序生成，它利用前一跳的执行结果来检索支持事实并迭代生成后续程序。我们在 MMQA-T^2 上评估了我们的模型，实验结果表明 HopPG 在多跳问题上优于现有的语义解析基线模型。