<div>
<div>
<div>
<p>Researchers studying persuasion have mostly focused on modeling arguments to
understand how people’s beliefs can change. However, in order to convince an audience the speakers usually adapt their speech. This can be seen often in political
campaigns when ideas are phrased - framed - in different ways according to the geo-graphical region the candidate is in. This practice suggests that, in order to change
people’s beliefs, it is important to take into account their previous perspectives and
topics of interest.
</p><p><br></p>
<p>In this work we propose ChangeMyStance, a novel task to predict if a user would
change their mind after being exposed to opposing views on a particular subject. This
setting takes into account users’ beliefs before a debate, thus modeling their preconceived notions about the topic. Moreover, we explore a new approach to solve the
problem, where the task is decomposed into ”simpler” problems. Breaking the main
objective into several tasks allows to build expert modules that combined produce
better results. This strategy significantly outperforms a BERT end-to-end model over
the same inputs.
</p>
</div>
</div>
</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9936260 |
| Date | 17 October 2019 |
| Creators | Aldo Fabrizio Porco (7460849) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/USING_MODULAR_ARCHITECTURES_TO_PREDICT_CHANGE_OF_BELIEFS_IN_ONLINE_DEBATES/9936260 |
Page generated in 0.0022 seconds