Grishma Jena, Cognitive Software Engineer with the Data Science for Marketing Team, IBM Watson Presented at MLconf 2018 Abstract: Personality and emotions play a vital role in defining human interactions. There has been a recent shift in making conversational agents and chatbots appear more human-like. Adding a persona to a chatbot is essential for this goal 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. Enterprise to Computer bot (E2Cbot) treats Star Trek dialog style and general dialog style differently by 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. To evaluate the bot, we use perplexity and word overlap with Star Trek vocabulary. We also perform further evaluation using human scores. This work is a joint project by Grishma Jena, Mansi Vashisht, João Sedoc and Abheek Basu under the guidance of Professor Lyle Ungar.