Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model
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.
Keywords
Cite
@article{arxiv.2004.11163,
title = {Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model},
author = {Stefan Ollinger and Lorik Dumani and Premtim Sahitaj and Ralph Bergmann and Ralf Schenkel},
journal= {arXiv preprint arXiv:2004.11163},
year = {2020}
}