  This paper presents a new deep learning architecture for Natural Language
Inference (NLI). Firstly, we introduce a new architecture where alignment pairs
are compared, compressed and then propagated to upper layers for enhanced
representation learning. Secondly, we adopt factorization layers for efficient
and expressive compression of alignment vectors into scalar features, which are
then 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
competitive performance on all. A lightweight parameterization of our model
also enjoys a $\approx 3$ times reduction in parameter size compared to the
existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining
competitive performance. Additionally, visual analysis shows that our
propagated features are highly interpretable.
