  The dialogue management is a task of conversational artificial intelligence.
The goal of the dialogue manager is to select the appropriate response to the
conversational partner conditioned by the input message and recent dialogue
state. Hybrid Code Networks is one of the models of dialogue managers, which
uses an average of word embeddings and bag-of-words as input features. We
perform experiments on Dialogue bAbI Task 6 and Alquist Conversational Dataset.
The experiments show that the convolutional neural network used as an input
layer of the Hybrid Code Network improves the model's turn accuracy.
