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Knowing the reasoning chains from knowledge to the predicted answers can helpconstruct an explainable question answering (QA) system. Advances on QAexplanation propose to explain the answers with entailment trees composed ofmultiple entailment steps. While current work proposes to generate entailmenttrees with end-to-end generative models, the steps in the generated trees arenot constrained and could be unreliable. In this paper, we propose METGEN, aModule-based Entailment Tree GENeration framework that has multiple modules anda reasoning controller. Given a question and several supporting knowledge,METGEN can iteratively generate the entailment tree by conducting single-stepentailment with separate modules and selecting the reasoning flow with thecontroller. As each module is guided to perform a specific type of entailmentreasoning, the steps generated by METGEN are more reliable and valid.Experiment results on the standard benchmark show that METGEN can outperformprevious state-of-the-art models with only 9% of the parameters.

METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation