论文标题
论证的内在质量评估
Intrinsic Quality Assessment of Arguments
论文作者
论文摘要
已经研究了自然语言论证的几个质量维度。有些可能反映在语言特征（例如，论证的安排）中，而另一些则依赖上下文（例如相关性）或主题知识（例如，可接受性）。在本文中，我们研究了15个维度的内在计算评估，即仅从论点的文本中学习。在现有语料库中具有八种特征类型的系统实验中，我们观察到大多数维度的中等但显着的学习成功。言辞质量似乎最难评估，并且主观性特征表现出强大，尽管语料库的长度偏差会阻碍全部有效性。我们还发现，人类评估师的差异与我们的方法更为明显。
Several quality dimensions of natural language arguments have been investigated. Some are likely to be reflected in linguistic features (e.g., an argument's arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability). In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument's text. In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions. Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity. We also find that human assessors differ more clearly to each other than to our approach.
