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

TL;DR

This paper introduces a BERT-based approach for the same side stance classification task, improving argument stance similarity detection without relying on topic-specific vocabulary, by fine-tuning BERT on argument pairs.

Contribution

It presents a novel application of fine-tuned BERT for stance classification based on argument similarity, simplifying stance detection without topic-specific vocabulary.

Findings

Achieved effective stance classification using BERT.

Fine-tuning BERT for three epochs improves accuracy.

Utilized argument pairs to assess stance similarity.

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.