LASTIST: LArge-Scale Target-Independent STance dataset

TL;DR

This paper introduces LASTIST, a large-scale Korean stance detection dataset, enabling research in target-independent stance detection and addressing low-resource language challenges.

Contribution

The paper presents the creation of the first large-scale Korean stance dataset, LASTIST, and demonstrates its utility for various stance detection tasks.

Findings

Successfully collected 563,299 labeled Korean sentences.

Trained state-of-the-art deep learning models on the dataset.

Enabled target-independent and diachronic stance detection in Korean.

Abstract

Stance detection has emerged as an area of research in the field of artificial intelligence. However, most research is currently centered on the target-dependent stance detection task, which is based on a person's stance in favor of or against a specific target. Furthermore, most benchmark datasets are based on English, making it difficult to develop models in low-resource languages such as Korean, especially for an emerging field such as stance detection. This study proposes the LArge-Scale Target-Independent STance (LASTIST) dataset to fill this research gap. Collected from the press releases of both parties on Korean political parties, the LASTIST dataset uses 563,299 labeled Korean sentences. We provide a detailed description of how we collected and constructed the dataset and trained state-of-the-art deep learning and stance detection models. Our LASTIST dataset is designed for various tasks in stance detection, including target-independent stance detection and diachronic evolution stance detection. We deploy our dataset on https://anonymous.4open.science/r/LASTIST-3721/.