This is Diane's practice talk for the ACL conference.
Abstract: We examine the utility of speech and lexical features for automatically predicting student emotions in human-computer spoken tutoring dialogues. We first annotate studentturns for negative, neutral, positive and mixed emotions.
We then extract acoustic-prosodic features from the speech signal, and lexical
items from the transcribed or recognized speech. We compare the results of
machine learning experiments using these features alone or in combination to
predict various categorizations of the annotated emotions. Our best results yield a 19-36% relative improvement in error reduction over a baseline. Finally, we compare our results with predicting emotion in human-human dialogues.
Carol Nichols
Karina Ivanetich
We are sharing this date to present our DMP summer research.
Carol's project is creating a test bed for collecting word alignment data from bilingual speakers of English and Chinese for use by a machine translator. This program will also gather data on how sure the people providing the word alignments are about their alignments and how long it took them, and this information would be useful to experimenters studying machine translation and word alignments.
Karina's Abstract:
Some languages (such as English) are rich in annotated resources, while many other languages experience a shortage or absence of annotated data. In addition, human annotation, although highly accurate, is costly in terms of both time and money. Researchers have created systems that utilize well-annotated languages in order to project POS tags onto other languages. However, the result has often been less than accurate. Researchers David Yarowksy and Grace Ngai have added to traditional projection algorithms, and for English-to-French projections, have obtained much higher levels of accuracy.
In my work here this summer, I will attempt to replicate their results, this time for English-to-Chinese projections. Since translation issues differ between these two sets of languages, I am expecting that I will need to improve the model to better serve the English-to-Chinese projections. My presentation will discuss this proposal as well as current progress.