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

arXiv: https://arxiv.org/abs/2004.11163 · HTML: https://mlsanity.com/paper/2004.11163/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study](https://mlsanity.com/paper/2110.07693/) (2021)
- [Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study](https://mlsanity.com/paper/2404.03987/) (2024)

## Abstract (arXiv)

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.