通过迭代逆向推理生成可解释的证明

Hanhao Qu, Yu Cao, Jun Gao, Liang Ding, Ruifeng Xu
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引用次数: 11

摘要

本文提出了一种迭代后向推理模型IBR,用于解决基于规则的问答(QA)中的证明生成任务,该模型需要对一系列文本规则和事实进行推理,以找出相关的证明路径并推导出最终答案。我们从两个方面解决了现有工作的局限性:1)通过从问题迭代向后预测证明路径中的节点和边,通过详细跟踪增强推理过程的可解释性;2)通过对节点和历史路径的精细表示进行推理,提高效率和准确性,而不需要在证明生成过程中引入任何可能引入外部噪声的中间文本。在IBR、QA和证明策略预测中有三个主要模块来获得答案并为后续程序提供指导;父节点预测,以确定现有节点中的一个新的子节点将链接到的证明;子节点预测,以找出哪个新节点将被添加到证明中。在合成数据集和释义数据集上的实验表明,IBR在域内性能和跨域可移植性方面都优于几种强基线。我们的代码和模型可在https://github上获得。com/find-knowledge/IBR。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretable Proof Generation via Iterative Backward Reasoning
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.
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