论文标题
METGEN：一个基于模块的需要树生成框架，用于答案说明
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation
论文作者
论文摘要
了解从知识到预测答案的推理链可以帮助构建一个可解释的问题答案（QA）系统。质量检查解释的进步建议用由多个需要步骤组成的需要树来解释答案。虽然当前的工作建议用端到端的生成模型生成需要树，但生成的树中的步骤不受限制，可能是不可靠的。在本文中，我们提出了METGEN，这是一个基于模块的RESTERMENT TREE生成框架，该框架具有多个模块和一个推理控制器。考虑到一个问题和几个支持知识，Metgen可以通过单独的模块进行单步操作并使用控制器选择推理流来迭代产生元素树。由于每个模块都被指导执行特定类型的组成推理，因此METGEN生成的步骤更加可靠和有效。标准基准测试的实验结果表明，METGEN只有9％的参数可以胜过以前的最先前模型。
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.
