Hybrid Code Networks using a convolutional neural network as an input layer achieves higher turn accuracy
Table 1: Model’s hyperparameters achieving the highest turn accuracy on the validation set of Dialogue bAbI Task 6
['[BOLD] Parameter/Model', '[BOLD] fastText', '[BOLD] fastText+CNN', '[BOLD] fastText+RNN', '[BOLD] word2vec', '[BOLD] word2vec+CNN', '[BOLD] word2vec+RNN']
We performed the hyperparameter optimization using Bayesian optimization on the validation part of the dataset. We performed 30 rounds of training with a different set of hyperparameters for each model. Each training was 30 epochs long. We evaluated the performance of the model after each epoch and saved the weights of the model achieving the highest turn accuracy on the validation set for each set of hyperparameters. We selected the hyperparameters achieving the highest turn accuracy.