论辩关系分析中的内容与语境剖析
Dissecting Content and Context in Argumentative Relation Analysis
摘要：
当评估论证单位之间的关系时（例如，支持或攻击），计算系统经常利用不是基本论证单元（EAU）本身的一部分，而是从其上下文（段落中的位置，前面的标记等）获得的公开指示符或标记。我们表明，这种依赖性比以前假设的要强得多。事实上，我们表明，通过完全掩盖EAU文本跨度，只从上下文中提供信息，竞争系统可能会发挥更好的作用。我们认为，一个论点分析系统，更依赖于话语背景比论点的内容是不安全的，因为它很容易被欺骗。为了缓解这个问题，我们从他们的上下文分离的争论单位，使系统被迫建模，并依赖于EAU的内容。我们表明，由此产生的分类系统是更强大的，并认为，这种模型更适合预测争论的关系，跨文件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
