A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915022736719872 |
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| author | Yuan, Jiaqing Xi, Ruijie Singh, Munindar P. |
| author_facet | Yuan, Jiaqing Xi, Ruijie Singh, Munindar P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08900 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media Yuan, Jiaqing Xi, Ruijie Singh, Munindar P. Computation and Language Artificial Intelligence 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. |
| title | A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.08900 |