Carol: abstract TBA
Theresa: Annotating Attributions and Private States
The slides for the Maximum Entropy tutorial that was held today during the ITSPOKE meeting can be found here.
Mihai
A Landscape Model analysis, adopted from the text processing
literature, was run on transcripts of tutoring sessions, and a
technique developed to count the occurrence of key physics points in
the resulting connection matrices. This point-count measure was found
to be well correlated with learning.
ABSTRACT
In this paper we take advantage of the
availability of a large amount of manually
annotated data to analyze the applicability
of Co-training (Blum and Mitchell,
1998) for predicting emotions with Spoken
Dialogue Data. The manual annotations
yielded the Upper Bounds of Cotraining
that show the trade-off between
the size of the initial train set and the accuracy
obtained by the final train set generated
with this method when adding examples
based on agreements, disagreements
and confidence of the predictions. We
found that in the ideal case, disagreements
would lead to build a more robust system,
but there is a gap between the Upper
Bounds and the behavior of Co-training
that lays on the amount of incorrect examples
added by the system. Our best
results achieve a maximum accuracy improvement
of 18.51% over the majority
class baseline, and 1.49% of improvement
over the accuracy of the initial train set.
This year's Speech Communications Best Paper Award is for paper:
Julia Hirschberg, Diane Litman, Marc Swerts, "Prosodic and Other Cues
to Speech Recognition Failures", Speech Communication, 43(1-2):155-176, 2004.