Siamese Neural Networks with Random Forest for detecting duplicate question pairs
Table 2: Comparison of performance of our models
['[BOLD] Method', '[BOLD] Cross-Entropy loss']
The base Siamese-GRU model is used in conjunction with the 3 features described above as input for a secondary classifier. We experimented with AdaBoost, SVM and Random Forest classifiers. The best results were obtained by the Random Forest with suitable regularization in the form of restriction of maximum tree depth. The score is calculated by Kaggle’s engine using cross-entropy loss. The model nomenclature states the number of stacked GRU layers followed by the number of fully-connected layers leading to the similarity node. Dropout has been used between fully-connected layers where mentioned.