text: 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.
  @cite_19: We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.
  @cite_14: Recurrent neural networks (RNNs) with Long Short-Term memory cells currently hold the best known results in unconstrained handwriting recognition. We show that their performance can be greatly improved using dropout - a recently proposed regularization method for deep architectures. While previous works showed that dropout gave superior performance in the context of convolutional networks, it had never been applied to RNNs. In our approach, dropout is carefully used in the network so that it does not affect the recurrent connections, hence the power of RNNs in modeling sequences is preserved. Extensive experiments on a broad range of handwritten databases confirm the effectiveness of dropout on deep architectures even when the network mainly consists of recurrent and shared connections.
  @cite_4: The dropout technique is a data-driven regularization method for neural networks. It consists in randomly setting some activations from a given hidden layer to zero during training. Repeating the procedure for each training example, it is equivalent to sample a network from an exponential number of architectures that share weights. The goal of dropout is to prevent feature detectors to rely on each other. Dropout has successfully been applied to Deep MLPs and to convolutional neural networks, for various tasks of Speech Recognition and Computer Vision. We recently proposed a way to use dropout in MDLSTM-RNNs for handwritten word and line recognition. In this paper, we show that further improvement can be achieved by implementing dropout differently, more specifically by applying it at better positions relative to the LSTM units.
  @cite_10: 
summary: @cite_4 studied dropout at different places with respect to the LSTM units in the network proposed by @cite_14 for handwriting recognition. The results show that significant performance difference is observed when dropout is applied to distinct places. They concluded that applying dropout only after recurrent layers (as applied by @cite_14 ) or between every feed-forward layer (as done by @cite_19 ) does not always yield good results. @cite_10 , investigated the effect of applying dropout in LSTMs. They randomly switch off the outputs of various gates of LSTM, achieving an optimal word error rate when dropout is applied to output, forget and input gates of the LSTM.