值得检查的跨主题索赔检测用于自动的事实核查
Amani S Abumansour1,2, Arkaitz Zubiaga1
1Queen Mary University of London, London, United Kingdom.
PeerJ. Computer science
|June 22, 2023
概括
自动化事实检查需要对新主题进行更好的检查价值检测. 阿拉CWA模型通过使用少量射击学习和数据增强来提高未见的主题的性能.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 自动化事实检查系统依赖于索赔检查价值检测,以优先考虑需要验证的句子.
- 现有的研究往往忽视了在多样化和未见的主题中识别值得检查的索赔的挑战.
- 在将模型应用于新学科领域时,性能退化是一个重要的问题.
研究的目的:
- 评估和量化检测值得检查的主题新,未见的值得检查的主张的挑战.
- 提出和评估AraCWA模型,以缓解跨主题索赔检查价值检测的性能下降.
- 提高事实检查系统对新领域的概括能力.
主要方法:
- 开发了AraCWA模型,结合了几次射击学习和数据增强技术.
- 利用公开可用的阿拉伯语推特数据集,涵盖14个不同的话题.
- 量化了跨主题索赔检查价值检测的挑战,并分析了主题相似性.
主要成果:
- 在AraCWA中提出的数据增强策略显著改善了跨主题的性能.
- 性能增长在不同主题上有所不同,突出了特定领域的挑战.
- 对语义主题相似性的分析表明了预测主题难度的潜在指标.
结论:
- AraCWA模型有效地提高了检测新主题值得检查的声明的性能.
- 数据增强是改善索赔检查价值检测中的跨主题概括的关键组成部分.
- 语义相似性分析可以作为一个有用的代理来估计这个任务中未见的主题的难度.
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