April 21, 2004

[TALK]: Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources

Diane will give a practice talk (about 20 minutes) of our HLT-NAACL paper:

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TITLE
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Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources
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AUTHORS
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Kate Forbes-Riley and Diane Litman
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ABSTRACT
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We examine the utility of multiple types of turn-level and contextual
linguistic feature s for automatically predicting student emotions in
human-human spoken tutoring dialogues. We first annotate student
turns in our corpus for negative, neutral and positive emotions. We
then automatically extract features representing acoustic-prosodic and
other linguistic information from the speech signal and associated
transcriptions. We compare the results of a variety of machine
learning experiments using different feature sets to predict the
annotated emotions. Our best performing feature set contains both
acoustic-p rosodic and other types of linguistic features, extracted
from both the current turn and a context o f previous student turns.
This feature set yields a prediction accuracy of 84.75%, which is a
44% relative improvement in error reduction over a baseline. Our
results suggest that the intelligent tutoring spoken dialogue system
we are developing can be enhanced to automatically predict and adapt
to student emotions.

Posted by litman at 12:00 PM

April 07, 2004

Spring Symposium Report

On April 7, Jan will summarize the AAAI Spring Symposium on

Exploring Attitude and Affect in Text: Theories and Applications

Posted by litman at 12:00 PM

April 04, 2004

April 01, 2004

[talk] Regina Barzilay's visit

Regina Barzilay will be a guest speaker in the Department of Computer Science colloquium series. She will be here on both 4/1 and 4/2.

NOTE: The talk is on Thurs. afternoon (4/1), not Friday morning

>
> What: Learning to Model Text Structure
> When: 4/1 at 3:30pm, refreshments at 3
> Where: SENSQ 5317/9
>
> Talk abstract:
>
> The natural language processing community has struggled for years to
> develop computational models of text structure. Such models are essential
> both for interpretation of human-written text and for evaluation of
> machine-generated text. Applications such as text summarization and
> machine translation would greatly benefit from such models.
>
> In this talk, I will present our first steps towards learning to model
> text structure. I will describe two models that are induced from a large
> collection of unannotated texts. The first model captures the notion of
> text cohesion by considering connectivity patterns characteristic of
> well-formed texts. These patterns are inferred from a matrix that
> combines distributional and syntactic information about text entities. The
> second model captures the content structure of texts within a specific
> domain, in terms of the topics the texts address and the order in which
> these topics appear. I will present an effective method for learning
> content models, utilizing a novel adaptation of algorithms for Hidden
> Markov Models. To conclude my talk, I will show how these text models can
> be effectively integrated into natural language generation and
> summarization systems.
>
> This is joint work with Mirella Lapata and Lillian Lee.
>
>

Posted by hwa at 03:30 PM