HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledge
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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.
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