期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：Verbal feedback is an important information
source in establishing interactional rapport.
However, predicting verbal feedback across
languages is challenging due to languagespecific
differences, inter-speaker variation,
and the relative sparseness and optionality of
verbal feedback. In this paper, we employ an
approach combining classifier weighting and
SMOTE algorithm oversampling to improve
verbal feedback prediction in Arabic, English,
and Spanish dyadic conversations. This approach
improves the prediction of verbal feedback,
up to 6-fold, while maintaining a high
overall accuracy. Analyzing highly weighted
features highlights widespread use of pitch,
with more varied use of intensity and duration.
