Title: Natural Language Generation for Intelligent Tutoring Systems: A Case Study
Speaker: Dr. Barbara DiEugenio, University of Illinois at Chicago
When: Thursday, July 7, 10:00am
Where: Sennott Square 5317, University of Pittsburgh
Abstract:
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It is still an open question whether Natural Language (NL) interaction
between students and an Intelligent Tutoring System (ITS) improves
learning, and if yes, what specific features of the NL interaction are
responsible for the improvement. To investigate this issue, we developed
two different feedback generation engines for an ITS that teaches students
to troubleshoot complex systems. We systematically evaluated the two NL
interfaces in a three way comparison that included the original ITS as
well. We found that the version of the ITS which intuitively produces the
best language does engender the most learning. Specifically, it appears
that presenting feedback at a more abstract level is responsible for the
improvement.
This will be my practice talk for the paper I will present at the AAAI Workshop on Question Answering in Restricted Domains.
Title:
Improving Question Answering for Reading Comprehension Tests by Combining Multiple Systems
Abstract:
Most work on reading comprehension question answering systems has focused on improving performance by adding complex natural language processing (NLP) components to such systems rather than by combining the output of multiple systems. Our paper empirically evaluates whether combining the outputs of seven such systems submitted as the final projects for a graduate level class can improve over the performance of any individual system. We present several analyses of our combination experiments, including performance bounds, impact of both tie-breaking methods and ensemble size on performance, and an error analysis. Our results, replicated using two different publicly available reading test corpora, demonstrate the utility of system combination via majority voting in our restricted domain question answering task.