This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new compare-propagate architecture where alignments pairs are compared and then propagated to upper layers for enhanced representation learning. Secondly, we adopt novel factorization layers for efficient compression of alignment vectors into scalar valued features, which are then be used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving state-of-the-art performance on all. A lightweight parameterization of our model enjoys a $\approx 300\%$ reduction in parameter size compared to the ESIM and DIIN, while maintaining competitive performance. Visual analysis shows that our propagated features are highly interpretable, opening new avenues to explainability in neural NLI models.

本文提出了一种新的深度学习架构来进行自然语言推理，并且采用因子分解层来提高表征学习的效率。实验表明，该架构在三个常用评估基准上取得了出色表现，并且可以实现轻量级参数化。此外，我们的可视化分析表明，我们的传播特征是高度可解释的。

比较、压缩和传播：利用对齐因式分解增强神经体系结构用于自然语言推理