The APVA-TURBO Approach To Question Answering in Knowledge Bases
PRESENTER:
Yongyi Mao
University of Ottawa
ABSTRACT:
In this work, we study the problem of question answering over a knowledge
base. We identify that the primary bottleneck in the current art towards
solving this problem lies in the difficulty of accurately predicting the
relations connecting the subject entity to the object entities. We
advocate a new model architecture, APVA, which includes a verification
mechanism responsible for checking the correctness of predicted relations.
The APVA framework naturally supports a well-principled iterative training
procedure, which we call turbo training. We demonstrate via experiments
that the APVA-TUBRO approach drastically improves the question answering
performance, establishing itself as the new state of art. This is a joint
work with Yue Wang and Richong Zhang at Beihang University, China.
Bio: Yongyi Mao received his Bachelor of Engineering degree at the
Southeast University (Nanjing, China) in 1992. In 1995, he received his
medical degree at Nanjing Medical University (Nanjing, China). In 1998,
Yongyi Mao obtained his Master of Science degree at the University of
Toronto, in the Department of Medical Biophysics . In 2003, he completed
his PhD in electrical engineering at the University of Toronto and joined
the faculty of School of EECS at the University of Ottawa as an Assistant
Professor. He was promoted to Associate Professor in 2008 and then to Full
Professor in 2012. Yongyi Mao's research interest at this moment includes
machine learning, information theory and their applications in knowledge
bases and natural language processing.