论文标题
在参数中检测可攻击的句子
Detecting Attackable Sentences in Arguments
论文作者
论文摘要
在争论中找到可攻击的句子是成功反驳论证的第一步。我们对在线参数中的句子攻击性进行了首次大规模分析。我们分析了在论证中攻击的驱动原因，并确定句子的相关特征。我们证明句子的攻击性与有关句子内容，命题类型和语气的许多特征有关，并且外部知识源可以提供有关攻击性的有用信息。在这些发现的基础上，我们证明了机器学习模型可以自动检测到参数中的攻击句子，这比几个基线要好得多，而且与外行人相当。
Finding attackable sentences in an argument is the first step toward successful refutation in argumentation. We present a first large-scale analysis of sentence attackability in online arguments. We analyze driving reasons for attacks in argumentation and identify relevant characteristics of sentences. We demonstrate that a sentence's attackability is associated with many of these characteristics regarding the sentence's content, proposition types, and tone, and that an external knowledge source can provide useful information about attackability. Building on these findings, we demonstrate that machine learning models can automatically detect attackable sentences in arguments, significantly better than several baselines and comparably well to laypeople.
