  Research on computational argumentation is currently being intensively
investigated. The goal of this community is to find the best pro and con
arguments for a user given topic either to form an opinion for oneself, or to
persuade others to adopt a certain standpoint. While existing argument mining
methods can find appropriate arguments for a topic, a correct classification
into pro and con is not yet reliable. The same side stance classification task
provides a dataset of argument pairs classified by whether or not both
arguments share the same stance and does not need to distinguish between
topic-specific pro and con vocabulary but only the argument similarity within a
stance needs to be assessed. The results of our contribution to the task are
build on a setup based on the BERT architecture. We fine-tuned a pre-trained
BERT model for three epochs and used the first 512 tokens of each argument to
predict if two arguments share the same stance.
