This thesis presents the design and architecture of an Active Learning system for Question Answering on Multiparty Dialogue. The goal of this system is to collect a robust Question Answering dataset and to improve the performance of the system on Question Answering challenges on Multiparty Dialogue. The system has an interactive web-based user interface which allows users to challenge the system with their own questions regarding a short passage of dialogues between multiple characters in a TV series. This system makes use of a state-of-art Machine Learning model to predict the answers to users’ questions. In the same time, the system learns from users’ responses and performs online update on the model. The system uses probability functions to guide user towards contributing data needed most for model improvement. The system is designed to handle high internet traffic by efficiently storing data and by carefully synchronizing the shared resources in the web system. The system has shown promising results in guiding users to contribute high quality data useful for model training.
Computer Science / Emory University
BS / Spring 2019
Jinho D. Choi, Computer Science and QTM, Emory University (Chair)
Shun Yan Cheung, Computer Science, Emory University
Ken Mandelberg, Computer Science, Emory University
Michael Carr, Medicine, Emory University
Anthology | Paper | Presentation