Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model

• URL: http://arxiv.org/abs/2004.11163v1
• Date: Thu, 23 Apr 2020 13:54:31 GMT
• Title: Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model
• Authors: Stefan Ollinger, Lorik Dumani, Premtim Sahitaj, Ralph Bergmann, Ralf Schenkel
• Abstract summary: The same side stance classification task provides a dataset of argument pairs classified by whether or not both arguments share the same stance. 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.
• Score: 8.8896707993459
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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