Our group meets on Thursdays at 11:00 a.m., FL 246, Beckett Conf. Room.
Dates and topics for this semester are as follows:
- Aug 23
- AIRG Organizational Meeting (Coordinator: Joe Beck)
- Aug 30
- AI Faculty
- "Research Overview"
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- Sept 6
- Dmitry Berenson
- topic tba
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- Sept 13
- Joe Beck
- "Graduate Student Survival Skills"
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- Sept 20
- Dongqing Xiao
- topic tba
Machine translation evaluation plays a great role as "Moses" in the
development of machine translation techniques. However, the performance of
state-of-art string similarity-based MT evaluation metric is invariably
considered to be inadequate. Although, researchers have successfully
applied linguistic features to reinforce it, the gain of performance is
limited. As MT continues to develop, there are various online machine
translation services which have become the routine use for web users. This
process is producing user feedback on a large scale. User feedback has
often been proposed as a solution for improving machine translation
systems. The basic motivation is that users will be able to make
disambiguating choices, take post-edit actions which fall outside of the
machine translation system's capabilities, or supply extra-linguistic
knowledge necessary for language analysis or language generation. Until
now, relative researches on utilizing expert feedback in MT automatic
evaluation have demonstrated the profit of user feedback while ignoring the
exploiting the fuzzy feedback from ordinary users. With the help of
community wise and quality control, ordinary user feedback seems to be a
good substitution of expert feedback. Whether or not it would service to
evaluate machine translation output more user-centered and closer to expert
evaluation than the state of art MT evaluation metric? How to get rid of
noise, and then make use of user feedback to improve machine translation
system?
- Sept 27
- Yutao Wang
- Learning and forgetting in ASSISTments
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The talk is about a variety of machine learning approaches she has
used to try to understand student learning and forgetting in
ASSISTments, a web-based tutor for mathematics.
- Oct 4
- -
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- Oct 11
- Dave Brown
- "Creativity, Surprise And Design"
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How can designer surprise an evaluator with their designs? How can
they surprise themselves with their own designs? Why do we care about
these questions? What has surprise got to do with creativity? How does
this relate to AI?
- Oct 18
- Hien Duong
- "AI in Accounting"
- ** Talk postponed **
- (Fall Recess - Oct 12-22)
- Oct 25
- Xiaolu Xiong
(work he has done with Shoujing Li)
- "All about ARRS: The development, problems and analysis"
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The new version of ARRS (Automatic Reassessment and Relearning
System) has been officially deployed on September 1st, 2012, and it is
being used by more than 150 math classes. The ARRS is an extension of the
ARRS system, the propose of the ARRS system is to make sure that students
well mastery the skill they have been taught, and after long period of
time, they still remember and understand these skills. It works by
automatically assigning students Reassessment test base on a certain
schedule. And if students fail on such tests, the system will also
automatically create and assign new Relearning assignments to them so that
they have the opportunity of reviewing the tutoring again. Although that we
have a set of defined settings to define the behavior of ARRS, and it is
acceptable by most teacher who are using the system, we are still very
interested in finding the set of settings which help the teaching and
learning the best. We propose a serious of experiment questions to address
to several important area of student learning and forgetting in the system,
such as when is the best time to reassess students, which skills should we
reassess them, does relearning matters, should we make ARRS smarter so it
use different set of setting on different group of students. As the
beginning stage of the research, we have already looked the relationship
between student mastery performance and Reassessment test performance; we
are expecting to have a better understanding on student short term
retention and find a better setting for the first Reassessment test.
- Nov 1
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- Nov 8
- Junjie Gu & Bin Lang
- topics tba
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- Nov 15
- -
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- Nov 22
- Thanksgiving Break - No Meeting
- Nov 29
- -
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- Dec 6
- Yue Gong
- "Improving an Intelligent Tutoring System Using Data Mining Techniques"
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PhD dissertation proposal
- Dec 13
- (hold for poss. thesis presentation)
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- -- Fri Dec 14 -- Last day of Semester
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