Multi-relation Question Answering is a challenging task, due to the requirement of elaborated analysis on questions and reasoning over multiple fact triples in knowledge base. In this paper, we present a novel model called Interpretable Reasoning Network that employs an interpretable, hop-by-hop reasoning process for question answering. The model dynamically decides which part of an input question should be analyzed at each hop; predicts a relation that corresponds to the current parsed results; utilizes the predicted relation to update the question representation and the state of the reasoning process; and then drives the next-hop reasoning. Experiments show that our model yields state-of-the-art results on two datasets. More interestingly, the model can offer traceable and observable intermediate predictions for reasoning analysis and failure diagnosis, thereby allowing manual manipulation in predicting the final answer.

翻译：多关系问题解答是一项艰巨的任务,因为需要详细分析知识库中多重事实三重的问题和推理。在本文中,我们提出了一个名为“解释性理由网络”的新模式,它使用可解释的、跳跃式推理解答程序进行解答。模型动态地决定了每个跳跃应分析输入问题的哪一部分;预测了与当前分析结果相对应的关系;利用预测关系更新问题陈述和推理过程的状况;然后驱动下一轮推理。实验显示,我们的模型在两个数据集中产生了最新的最新结果。更有趣的是,该模型可以为推理分析和失败诊断提供可追踪和可观测的中间预测,从而允许在预测最终答案时进行人工操纵。