Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model
This work addresses a specific bottleneck in computational argumentation for researchers and developers by providing a more reliable method for stance classification, though it is incremental as it builds on existing BERT fine-tuning techniques.
The paper tackled the problem of unreliable pro/con classification in argument mining by introducing the same side stance classification task, which assesses whether two arguments share the same stance without needing topic-specific vocabulary, and achieved results by fine-tuning a BERT model for three epochs on the first 512 tokens of each argument.
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.