CsFEVER和CTKFacts:获取捷克数据用于事实验证
Herbert Ullrich1, Jan Drchal1, Martin Rýpar1
1Artificial Intelligence Center, Faculty of Electrical Engineering, Czech Technical University in Prague, Charles Square 13, 120 00 Prague 2, Czech Republic.
概括
本研究介绍了获取捷克数据用于自动事实检查的方法,创建了CsFEVER-NLI和CTKFactsNLI.LI等数据集. 这些资源有助于分类文本声明真实性和自然语言推理任务.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 信息检索 信息检索
背景情况:
- 自动的事实检查需要标记的数据集来训练真实性分类模型.
- 现有的数据集往往是特定于语言的,需要为像捷克语这样的代表性不足的语言创建资源.
研究的目的:
- 开发和评估获取捷克数据用于自动化事实核查的方法.
- 创建新的捷克数据集,用于事实检查和自然语言推理 (NLI).
- 分析数据集的潜在偏差,并提供基线模型.
主要方法:
- 机器翻译和文档对齐的混合方法,以创建FEVER数据集 (CsFEVER-NLI) 的捷克版本.
- 使用捷克新闻社文章对新数据集 (CTKFacts) 进行注释,然后进行清理和错误分析.
- 从获得的数据中开发独立的NLI数据集 (CsFEVER-NLI,CTKFactsNLI).
主要成果:
- 发表了CsFEVER-NLI (127k个翻译) 和CTKFactsNLI数据集.
- 对虚假线索和注释器错误的数据集的分析,以及常见错误的类型.
- 为事实核查管道阶段提供基线模型.
结论:
- 开发的方法和数据集有助于在捷克进行自动的事实核查和NLI研究.
- 该研究强调了数据集质量和偏差分析对于强大的事实核查模型的重要性.
- 发表的资源和工具旨在推进跨语言事实核查能力.
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