Abstract

Recognizing when we need more information and asking clarifying questions are integral to communication in our day to day life. It helps us complete our mental model of the world and eliminate confusion. Chatbots need this technique to meaningfully collaborate with humans. We have investigated a process to generate an automated system that mimics human communication behavior using knowledge graphs, weights, an ambiguity test, and a response generator. It can take input dialog text and based on the chatbot's knowledge about the world and the user it can decide if it has enough information or if it requires more. Based on that decision, the chatbot generates a dialog output text which can be an answer if a question is asked, a statement if there are no doubts or if there is any ambiguity, it generates a clarifying question. The effectiveness of these features has been backed up by an empirical study which suggests that they are very useful in a chatbot not only for crucial information retrial but also for keeping the flow and context of the conversation intact.

Degree

MS

College and Department

Physical and Mathematical Sciences

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2020-07-31

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd11406

Keywords

knowledge graph, Stanford NLP annotators, chatbot, clarifying questions, ambiguity

Language

English

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