构建跨领域社交媒体论点立场分类基准，免人工标注。
A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media
- 用平台规则与大模型生成跨领域论点数据
- 覆盖21个领域，含4498个议题与30961条论据
- 支持零样本与少样本学习测试，适合立场分析研究
论点立场分类在识别作者对特定话题观点中起关键作用。然而，跨多个领域生成多样化的论点句子对极具挑战性。现有基准多源于单一领域或仅涵盖有限话题，且人工标注耗时费力。为此，我们提出利用平台规则、现成专家内容及大语言模型，绕过人工标注需求。该方法构建了一个多领域基准，包含4,498个主题性主张和30,961条论据，来自三个来源，覆盖21个领域。我们在全监督、零样本和少样本设置下对该数据集进行基准测试，揭示了不同方法的优劣。本研究已发布数据集与代码，具体信息见隐藏以匿名。
原文摘要 · Abstract (English)
Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies. We release the dataset and code in this study at hidden for anonymity.
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