UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish
Table 2: Parameter settings for the algorithms used. For feed-forward, we show the number of nodes per layer. The Dense column for LSTM shows whether a dense layer was added after the LSTM layers (with half the number of nodes as is shown in the Nodes column). The feed-forward networks always use a dropout of 0.001 after the first layer.
['[BOLD] Task', '[BOLD] SVM  [BOLD] Epsilon', '[BOLD] Feed-forward  [BOLD] Layers', '[BOLD] Feed-forward  [BOLD] Nodes', '[BOLD] LSTM  [BOLD] Layers', '[BOLD] LSTM  [BOLD] Nodes', '[BOLD] LSTM  [BOLD] Dropout', '[BOLD] LSTM  [BOLD] Dense']
For both the LSTM network and the feed-forward network, a parameter search was done for the number of layers, the number of nodes and dropout used. This was done for each subtask, i.e. different tasks can have a different number of layers. All models were implemented using Keras Chollet et al. After the best parameter settings were found, the results of 10 system runs to produce our predictions were averaged For the SVM (implemented in scikit-learn Each parameter search was performed using 10-fold cross validation, as to not overfit on the development set.