论文标题
通过迭代向后推理解释的证明产生
Interpretable Proof Generation via Iterative Backward Reasoning
论文作者
论文摘要
我们提出了IBR，这是一个迭代的向后推理模型，用于解决基于规则的问题答案（QA）的证明生成任务，其中需要模型来推理一系列文本规则和事实，以找出相关的证明路径并得出最终答案。我们通过两个折叠处理存在的作品的局限性：1）通过通过迭代路径中的节点和边缘从问题上预测节点和边缘来增强推理过程的解释性； 2）通过推理节点和历史路径的精心表示，促进效率和准确性，而没有任何中间文本，这些文本可能会在证明生成过程中引入外部噪声。 IBR，QA和证明策略预测中有三个主要模块，以获取答案并为以下程序提供指导；父节点预测在现有证据中确定一个新子节点将链接到的节点；儿童节点预测，以找出将添加哪些新节点。合成和释义数据集的实验表明，与几个强基础相比，IBR具有更好的内域性能以及跨域的转移性。我们的代码和型号可在https://github.com/find-knowledge/ibr上找到。
We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer. We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and history paths, without any intermediate texts that may introduce external noise during proof generation. There are three main modules in IBR, QA and proof strategy prediction to obtain the answer and offer guidance for the following procedure; parent node prediction to determine a node in the existing proof that a new child node will link to; child node prediction to find out which new node will be added to the proof. Experiments on both synthetic and paraphrased datasets demonstrate that IBR has better in-domain performance as well as cross-domain transferability than several strong baselines. Our code and models are available at https://github.com/find-knowledge/IBR .
