Computer Science Colloquium
Time+Place : Wednesday 21/12/2011 14:30 room 337-8 Taub  Bld.
Speaker    : Dan Roth SPECIAL LECTURE, Note unusual day
Affiliation: Computer Science and the Beckman Institute,
             University of Illinois at Urbana/Champaign
Host       : Shaul Markovitch
Title      : Learning from Natural Instructions
Abstract   :
Machine learning is traditionally formalized as the study of learning
concepts and decision functions from labeled examples. This requires
representations that encode information about the target function's domain.
We are interested in providing a way for a human teacher to interact with an
automated learner using natural instructions communicating relevant domain
expertise to the learner without necessarily knowing anything about the
internal representation used in the learning process. The underlying problem
becomes that of interpreting the natural language lesson in the context of
the task of interest.
This talk focuses on the machine learning aspects of this problem. The key
challenge is to learn intermediate structured representation - natural
language interpretations - without being given direct supervision at that
We will present research on Constrained Conditional Models (CCMs), a
framework that augments probabilistic models with declarative constraints in
order to support learning such interpretations. In CCMs we formulate natural
language interpretation problems as Integer Linear Programs, as a way to
assign values to sets of interdependent variables and perform
constraints-driven learning and global inference that accounts for the
In particular, we will focus on new algorithms for training these global
models using indirect supervision signals. Learning models for structured
tasks is difficult partly since generating supervision signals is costly. We
show that it is often easy to obtain a related indirect supervision signal,
and discuss several options for deriving this supervision signal, including
inducing it from the world's response to the model's actions, thus
supporting Learning from Natural Instructions.
We will explain and show the contribution of easy-to-get indirect
supervision to other NLP tasks such as Information Extraction,
Transliteration and Textual Entailment.
Short Bio:
Dan Roth is a Professor in the Department of Computer Science and the
Beckman Institute at the University of Illinois at Urbana-Champaign and a
University of Illinois Scholar. He is the director of a DHS Center for
Multimodal Information Access & Synthesis (MIAS) and also has faculty
positions in Statistics, Linguistics and at the School of Library and
Information Sciences.
Roth is a Fellow of AAAI for his contributions to the foundations of machine
learning and inference and for developing learning-centered solutions for
natural language processing problems. He has published broadly in machine
learning, natural language processing, knowledge representation and
reasoning, and learning theory, and has developed advanced machine learning
based tools for natural language applications that are being used widely by
the research community.
Prof. Roth has given keynote talks in major conferences, including AAAI,
EMNLP and ECML and presented several tutorials in universities and
conferences including at ACL and EACL. Roth was the program chair of
AAAI'11, CoNLL'02 and of ACL'03, and is or has been on the editorial board
of several journals in his research areas and has won several teaching and
paper awards.  Prof. Roth received his B.A Summa cum laude in Mathematics
from the Technion, Israel, and his Ph.D in Computer Science from Harvard
University in 1995.
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