论文标题
基于方面的论点挖掘
Aspect-Based Argument Mining
论文作者
论文摘要
一般而言，计算论证尤其是参数挖掘是重要的研究领域。在以前的作品中，解决了许多自动提取的挑战，并在某种程度上解决自然语言论点的原因。提取参数单元的工具越来越多，可以解决进一步的开放问题。在这项工作中，我们介绍了基于方面的参数挖掘（ABAM）的任务，并具有方面术语提取（ATE）和嵌套分割（NS）的基本子任务。首先，我们创建并发布带有令牌级别的方面信息的注释语料库。我们将各个方面视为要点单位正在解决的要点。此信息对于进一步的下游任务很重要，例如参数排名，参数摘要和生成，以及在该方面级别上搜索反对意见。我们使用最先进的监督体系结构进行了几项实验，并展示了它们在两个子任务中的性能。带注释的基准标准可在https://github.com/trtm/abam上获得。
Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using state-of-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM.
