  Human interactions and human-computer interactions are strongly influenced by
style as well as content. Adding a persona to a chatbot makes it more
human-like and contributes to a better and more engaging user experience. In
this work, we propose a design for a chatbot that captures the "style" of Star
Trek by incorporating references from the show along with peculiar tones of the
fictional characters therein. Our Enterprise to Computer bot (E2Cbot) treats
Star Trek dialog style and general dialog style differently, using two
recurrent neural network Encoder-Decoder models. The Star Trek dialog style
uses sequence to sequence (SEQ2SEQ) models (Sutskever et al., 2014; Bahdanau et
al., 2014) trained on Star Trek dialogs. The general dialog style uses Word
Graph to shift the response of the SEQ2SEQ model into the Star Trek domain. We
evaluate the bot both in terms of perplexity and word overlap with Star Trek
vocabulary and subjectively using human evaluators.
