  The generation of explanation graphs is a significant task that aims to
produce explanation graphs in response to user input, revealing the internal
reasoning process. This task is challenging due to the significant discrepancy
between unstructured user queries and structured explanation graphs. Current
research commonly fine-tunes a text-based pre-trained language model on a small
downstream dataset that is annotated with labeled graphs. However, due to the
limited scale of available datasets, this approach may prove to be insufficient
in bridging the gap between natural language text and structured graphs. In
this paper, to alleviate the above limitations, we propose a novel pre-trained
framework EG3P(for Explanation Graph Generation via Generative Pre-training
over synthetic graphs) for the explanation graph generation task. Specifically,
we first propose a text-to-graph generative task to pre-train the model with
the goal of bridging the text-graph gap. Additionally, we propose an automatic
corpus synthesis strategy for synthesizing a large scale of high-quality
corpus, reducing the reliance on costly manual annotation methods. Experimental
results on ExplaGraphs show the effectiveness of EG3P that our model surpasses
all baseline systems with remarkable margins. Besides, further analysis
demonstrates that EG3P is able to generate better explanation graphs on actual
reasoning tasks such as CommonsenseQA and OpenbookQA.
