  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 .
