论证关系分类的似然排序
Argumentative Relation Classification as Plausibility Ranking
摘要：
我们制定的论证关系分类（支持与攻击）作为一个文本的可扩展性排名任务。为此，我们提出了一个简单的重建技巧，使我们能够建立最小的对似是而非和难以置信的文本模拟自然的情况下，两个论证单位可能或不可能出现。我们表明，这种方法是有竞争力的，虽然它是相当简单的与以前的工作。在最近推出的基于内容的版本的任务，其中上下文话语线索被隐藏，该方法提供了超过10%的宏观F1的性能提高。相对于稀缺的攻击类，该方法实现了在查准率的大幅增加，而在召回率的损失很小，甚至不存在。
英文摘要：
We formulate argumentative relation classification (support vs. attack) as a text-plausibility ranking task. To this aim, we propose a simple reconstruction trick which enables us to build minimal pairs of plausible and implausible texts by simulating natural contexts in which two argumentative units are likely or unlikely to appear. We show that this method is competitive with previous work albeit it is considerably simpler. In a recently introduced content-based version of the task, where contextual discourse clues are hidden, the approach offers a performance increase of more than 10% macro F1. With respect to the scarce attack-class, the method achieves a large increase in precision while the incurred loss in recall is small or even nonexistent.
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。
