1 Introduction
Table 3: Model accuracy on train and tests sets.
['[EMPTY]', '[BOLD] Train  [BOLD] # Samples', '[BOLD] Train  [BOLD] Accuracy', '[BOLD] Test  [BOLD] # Samples', '[BOLD] Test  [BOLD] Accuracy', '[BOLD] Test  [BOLD] Precision', '[BOLD] Test  [BOLD] Recall', '[BOLD] Test  [BOLD] F-Score']
State-of-the-art monolingual information retrieval systems Huang et al. We extended their idea to build a network with two monolingual encoders for source and target each to extract semantic features followed by a CNN for syntactic features. Input to the model are 300 dimensional embeddings from pretrained FastText Bojanowski et al. We used two BiLSTMs for each encoder with the outputs of both LSTM concatenated. They were then sequentially fed to two convolution modules. CNN output was passed through a fully connected layer before making a three class prediction. We used ReLU activation with dropout and Batch Normalization. We used Adam optimizer Kingma and Ba in all our experiments with a batch size of 8192. We chose a learning rate of 10−3 scheduled to drop by a factor of 10 twice whenever the rate of training loss drop was less than 10−3 before stopping training. We observe that the model performs similarly with accuracy of above 91% for all five languages. This is possibly because shorter ones when paraphrased are harder to detect than longer ones.