When assessing relations between argumentative units (e.g., support or
attack), computational systems often exploit disclosing indicators or markers
that are not part of elementary argumentative units (EAUs) themselves, but are
gained from their context (position in paragraph, preceding tokens, etc.). We
show that this dependency is much stronger than previously assumed. In fact, we
show that by completely masking the EAU text spans and only feeding information
from their context, a competitive system may function even better. We argue
that an argument analysis system that relies more on discourse context than the
argument's content is unsafe, since it can easily be tricked. To alleviate this
issue, we separate argumentative units from their context such that the system
is forced to model and rely on an EAU's content. We show that the resulting
classification system is more robust, and argue that such models are better
suited for predicting argumentative relations across documents.

该研究论文指出通过遮盖文本本身，而只利用文本的上下文信息来分析争论单元所代表的争议关系的方法是不太可靠的。研究人员提出将争论单元与上下文分开，以更好地预测文档间的争论关系。