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Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model by Ollinger, Stefan; Dumani, Lorik; Sahitaj, Premtim; Bergmann, Ralph; Schenkel, Ralf is a scholarly article available to read on EtoBox.

What is Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model about?

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.

Author

Ollinger, Stefan; Dumani, Lorik; Sahitaj, Premtim; Bergmann, Ralph; Schenkel, Ralf

Published

2020

Language

EN