Applying NLP to Build a Cold Reading Chatbot

Abstract

Chatbots are computer programs designed to simulate conversation by interacting with a human user. In this paper we present a chatbot framework designed specifically to aid prolonged grief disorder (PGD) sufferers by replicating the techniques performed during cold readings. Our initial framework performed an association rule analysis on transcripts of real-world cold reading performances, in order to generate the required data as used in traditional rules based chatbots. However due to the structure of cold readings the traditional approach was unable to determine a satisfactory set of rules. Therefore, in this paper we discuss the limitations of this approach and subsequently provide a generative solution using sequence-to-sequence modeling with long short-term memory. We demonstrate how our generative chatbot is therefore able to provide appropriate responses to the majority of inputs. However, as inappropriate responses can present a risk to sensitive PGD sufferers we suggest a final iteration of our chatbot which successfully adjusts to account for multi-turn conversations.