  Dropout is a crucial regularization technique for the Recurrent Neural
Network (RNN) models of Natural Language Inference (NLI). However, dropout has
not been evaluated for the effectiveness at different layers and dropout rates
in NLI models. In this paper, we propose a novel RNN model for NLI and
empirically evaluate the effect of applying dropout at different layers in the
model. We also investigate the impact of varying dropout rates at these layers.
Our empirical evaluation on a large (Stanford Natural Language Inference
(SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward
connection severely affects the model accuracy at increasing dropout rates. We
also show that regularizing the embedding layer is efficient for SNLI whereas
regularizing the recurrent layer improves the accuracy for SciTail. Our model
achieved an accuracy 86.14% on the SNLI dataset and 77.05% on SciTail.
