Deep Semantics from Shallow Supervision
Speaker: Percy Liang , UC Berkeley
Date: March 7 2011
Time: 4:00PM to 5:00PM
Location: 32-G449
Host: Regina Barzilay and Leslie Kaelbling , CSAIL
Contact: Francis Doughty, 253-4602, doughty@mit.edu
What is the total population of the ten largest capitals in the US?
Building a system to answer free-form questions such as this requires
modeling the deep semantics of language. But to develop practical,
scalable systems, we want to avoid the costly manual annotation of
these deep semantic structures and instead learn from just
surface-level supervision, e.g., question/answer pairs. To this end,
we develop a new tree-based semantic representation which has
favorable linguistic and computational properties, along with an
algorithm that induces this hidden representation. Using our
approach, we obtain significantly higher accuracy on the task of
question answering compared to existing state-of-the-art methods,
despite using less supervision.
Biography
Percy Liang obtained a B.S. (2004) and an M.S. (2005) from MIT and is
now completing his Ph.D. at UC Berkeley with Michael Jordan and Dan
Klein. The general theme of his research, which spans machine
learning and natural language processing, is learning
richly-structured statistical models from limited supervision. He has
won a best student paper at the International Conference on Machine
Learning in 2008, received the NSF, GAANN, and NDSEG fellowships, and
is also a 2010 Siebel Scholar.
See other events that are part of CS Special Seminar Series 2010/2011
See other events happening in March 2011
