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@TECHREPORT{Carron_Idiap-Com-03-2020,
         author = {Carron, Daniel},
       projects = {Idiap},
          month = {8},
          title = {Deep Learning of Charisma},
           type = {Idiap-Com},
         number = {Idiap-Com-03-2020},
           year = {2020},
    institution = {Idiap},
       abstract = {The study of charisma in the field of leadership studies has been steadily growing after it was
shown to be a good predictor of success in the workplace and political setting. In order to
study what charisma is, one must be able to define character traits that can be observed and
quantified. In this paper, we will take a look at elements of speech that are used to measure how
charismatic a speaker is. Modern studies have defined a total of nine Charismatic Leadership
Tactics with metaphors, lists or contrasts being a few of them. Until now, these elements of
speech were marked by hand by trained human annotators from transcribed speeches they
wished to analyze. This is a long and costly process that takes a toll on the person executing it
and is prone to bias.
Here, we propose some methods using machine learning to perform the classification of text
into these different classes. We explore state-of-the-art models and analyze their efficiency
in performing this classification task. We begin by building a simple classifier with recurrent
networks and linear projections which performs some amount of classification but can be
largely improved on. We then introduce the mechanism of attention which greatly improves
the performance by being able to focus on relevant words in the input sentence. Taking
advantage of attention by increasing the number of heads looking at the sentences doesn’t
have a noticeable effect on the results. Lastly, we use transformers which are said to attain
state-of-the-art performance in text processing tasks and see that a BERT model slightly
improves on our simple attention model.},
            pdf = {https://publications.idiap.ch/attachments/reports/2020/Carron_Idiap-Com-03-2020.pdf}
}