Knowing the reasoning chains from knowledge to the predicted answers can help construct an explainable question answering (QA) system. Advances on QA explanation propose to explain the answers with entailment trees composed of multiple entailment steps. While current work proposes to generate entailment trees with end-to-end generative models, the steps in the generated trees are not constrained and could be unreliable. In this paper, we propose METGEN, a Module-based Entailment Tree GENeration framework that has multiple modules and a reasoning controller. Given a question and several supporting knowledge, METGEN can iteratively generate the entailment tree by conducting single-step entailment with separate modules and selecting the reasoning flow with the controller. As each module is guided to perform a specific type of entailment reasoning, the steps generated by METGEN are more reliable and valid. Experiment results on the standard benchmark show that METGEN can outperform previous state-of-the-art models with only 9% of the parameters.

翻译：了解从知识到预测答案的推理链有助于构建一个可以解释的问题解答(QA)系统。 QA解释的进展建议用包含多个导理步骤的树来解释答案。 虽然当前工作提议用端到端的基因模型来生成包含的树, 生成的树的步骤没有限制, 并且可能不可靠 。 在本文中, 我们提议了基于模块的基于模块的配料树精化框架, 有多个模块和一个推理控制器 。 鉴于一个问题和几个支持性知识, METGEN 可以通过使用单独的模块进行单步连接并选择与控制器的推理流程来迭代生成包含的树。 由于每个模块都被引导来实施特定类型的包含推理, METGEN 生成的步骤更加可靠和有效 。 标准基准的实验结果表明, METGEN 能够超过以前的状态模型, 且只有9%的参数。