论文标题
数据集独立的基线集，用于参数挖掘中的关系预测
A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining
论文作者
论文摘要
参数挖掘是旨在从文本中提取论证组成部分并预测论证关系（即支持和攻击）的研究领域。特别是，在文献中提出了许多方法，以预测论点之间的关系，并为此目的构建了特定于应用的注释资源。尽管已经创建了这些资源来实验相同的任务，但单个关系预测方法的定义将成功应用于这些数据集的很大一部分，这是参数挖掘的一个开放研究问题。这意味着文献中提出的任何方法都无法轻松地从一个资源移植到另一种资源。在本文中，我们通过提出一组独立的强大神经基准来解决此问题，这些神经基线可在文献中提出的所有数据集中获得有关论证关系预测任务的同一结果。因此，我们的基准可以由论证挖掘社区使用，以更有效地比较方法在论证关系预测任务上的表现如何。
Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i.e.,support and attack) from text. In particular, numerous approaches have been proposed in the literature to predict the relations holding between the arguments, and application-specific annotated resources were built for this purpose. Despite the fact that these resources have been created to experiment on the same task, the definition of a single relation prediction method to be successfully applied to a significant portion of these datasets is an open research problem in Argument Mining. This means that none of the methods proposed in the literature can be easily ported from one resource to another. In this paper, we address this problem by proposing a set of dataset independent strong neural baselines which obtain homogeneous results on all the datasets proposed in the literature for the argumentative relation prediction task. Thus, our baselines can be employed by the Argument Mining community to compare more effectively how well a method performs on the argumentative relation prediction task.
