A scaled‐down neural conversational model for chatbots

Summary Deep learning has revolutionized the field of conversation modeling. A lot of the research has been toward making the conversational agent more human‐like. As a result, overall the model size increases. Bigger models require more data and are costly to build and maintain. Often, for some tasks, high‐quality responses are not necessary. In this paper, a model that consumes fewer resources and a way to augment conversation data without increasing the size of the vocabulary is proposed. The proposed model uses a modified version of the GRU instead of the LSTM to encode and decode sequences of text.