tasksource/starcon

---
task_categories:
- text-classification
language:
- en
license: unknown
---
https://github.com/dwslab/StArCon
```
@inproceedings{kobbe-etal-2020-unsupervised,
title = "Unsupervised stance detection for arguments from consequences",
author = "Kobbe, Jonathan and
Hulpu{\textcommabelow{s}}, Ioana and
Stuckenschmidt, Heiner",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.4",
doi = "10.18653/v1/2020.emnlp-main.4",
pages = "50--60",
abstract = "Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.",
}
```

原始信息汇总

数据集概述

• 任务类别: 文本分类
• 语言: 英语
• 许可证: 未知

数据集来源

• 链接: https://github.com/dwslab/StArCon

数据集相关研究

• 论文标题: Unsupervised stance detection for arguments from consequences
• 作者: Kobbe, Jonathan; Hulpuş, Ioana; Stuckenschmidt, Heiner
• 发表会议: 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
• 发表时间: November 2020
• 出版商: Association for Computational Linguistics
• 论文摘要: 本文提出了一种无监督方法来检测与主题相关的论点的立场，特别关注于后果类论点。实验结果显示，该方法在某些方面甚至优于BERT，并发布了一个新的与后果相关的论点数据集，该数据集通过Amazon Mechanical Turk进行标注。