Combining deep learning with symbolic logic reasoning aims to capitalize on
the success of both fields and is drawing increasing attention. Inspired by
DeepLogic, an end-to-end model trained to perform inference on logic programs,
we introduce IMA-GloVe-GA, an iterative neural inference network for multi-step
reasoning expressed in natural language. In our model, reasoning is performed
using an iterative memory neural network based on RNN with a gate attention
mechanism. We evaluate IMA-GloVe-GA on three datasets: PARARULES, CONCEPTRULES
V1 and CONCEPTRULES V2. Experimental results show DeepLogic with gate attention
can achieve higher test accuracy than DeepLogic and other RNN baseline models.
Our model achieves better out-of-distribution generalisation than RoBERTa-Large
when the rules have been shuffled. Furthermore, to address the issue of
unbalanced distribution of reasoning depths in the current multi-step reasoning
datasets, we develop PARARULE-Plus, a large dataset with more examples that
require deeper reasoning steps. Experimental results show that the addition of
PARARULE-Plus can increase the model's performance on examples requiring deeper
reasoning depths. The source code and data are available at
this https URL.

本文提出了一种基于神经内存网络的迭代神经推理网络，使用门控机制进行推理，旨在解决多步推理中不平衡推理深度分布的问题，并将其应用于三个数据集上，实验结果表明该模型可以实现比 DeepLogic 和其他 RNN 基准模型更高的测试准确性，并在规则被洗牌时具有更好的超分布泛化能力。