Whatsapp Chatbot

EasyChair Preprint no. 3641

Abstract

Conversational modeling is an important task in natural language processing as well as machine learning. Like most important tasks, it’s not easy. Previously, conversational models have been focused on specific domains, such as booking hotels or recommending restaurants. They were built using hand-crafted rules, like ChatScript , a popular rule-based conversational model. In 2014, the sequence to sequence model being used for translation opened the possibility of phrasing dialogues as a translation problem: translating from an utterance to its response. The systems built using this principle, while conversing fairly fluently, aren’t very convincing because of their lack of personality and inconsistent persona . In this paper, we experiment building open-domain response generator with personality and identity. We built chatbots that imitate characters in popular TV shows: Barney from How I Met Your Mother, Sheldon from The Big Bang Theory, Michael from The Office, and Joey from Friends. A successful model of this kind can have a lot of applications, such as allowing people to speak with their favorite celebrities, creating more life-like AI assistants, or creating virtual alter-egos of ourselves. The model was trained end-to-end without any hand-crafted rules. The bots talk reasonably fluently, have distinct personalities, and seem to have learned certain aspects of their identity. The results of standard automated translation model evaluations yielded very low scores. However, we designed an evaluation metric with a human judgment element, for which the chatbots performed well. We are able to show that for a bot’s response, a human is more than 50% likely to believe that the response actually came from the real character.

Keyphrases: Artificial Intelligence, automated chatbot, Python

@Booklet{EasyChair:3641,
  author = {Gaurang Jindal and Dinesh Upadhyay and Ashish Jha},
  title = {Whatsapp Chatbot},
  howpublished = {EasyChair Preprint no. 3641},

  year = {EasyChair, 2020}}