Enterprise To Computer: Star Trek Chatbot

Jena Grishma, Vashisht Mansi, Basu Abheek, Ungar Lyle, Sedoc João. Arxiv 2017

[Paper]    
Reinforcement Learning

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.

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