FAME: A Framework for Argument Mining and Evaluation

目前对论证的研究有两种不同的视角，一种是从自然语言文本中挖掘论证的方法，另一种是形式论证评价的方法。到目前为止，这些研究领域在很大程度上是独立的、互不相关的。FAME项目的总体目标是将这两个视角及其各自的研究议程联系起来。我们开发了一个整合了论证挖掘和形式化论证评估的框架。这种连接将允许评估自然语言文本中的“深层”语义论元结构和对检索系统的新形式的查询。这里的挑战是弥合处理自然语言文本和用相应的形式语义评估以某种形式语言表达的论点之间的差距。这一建议背后的假设是，受控自然语言(CNL)可以为弥合这一差距提供必要的联系。CNL可以作为议论文的中间表征。它们具有自然语言的外观和感觉，因此足够接近基于自然语言的参数处理。另一方面，我们将使用的CNL也具有定义良好的形式语义，因此服从于形式评估方法。上述类型的CNL已经存在。一个突出的例子是Attempto受控英语(ACE)。ACE很容易被人类理解。同时，在转换为语篇表示结构的基础上对其语义进行了形式化定义，并进一步将其转换为谓词逻辑。由于ACE的开发主要没有考虑到论证，我们将彻底调查所提供的语言构造是否足以满足我们的目的，或者我们是否需要扩展该语言。在该项目中，我们调查公开辩论、报纸文本中的政治话语以及与选定的五个有争议的问题(例如自由贸易协定)有关的用户评论。通过将特定的经验性问题建模为知识库中的CNL语句，我们将现有的论元挖掘方法从识别一般的、功能类型的论元结构(如前提、主张)扩展到语义论元成分。同时，在知识库中将这样的成分表示为形式上可评估的陈述，允许与抽象论证评估方法相结合。我们希望这是一个合适的框架来研究特定的论元结构，因为它们通常出现在经验交际中(例如，不完整或不一致的论元)。

Two different perspectives on argumentation have been pursued in recent research, namely approaches of argument mining from natural language texts on the one hand and formal argument evaluation on the other. So far these research areas are largely independent and unrelated. The overall goal of the FAME project is to link these two perspectives and their respective research agendas. We develop a framework which integrates argument mining and formal argument evaluation. This connection will allow for evaluation of 'deep' semantic argument structures in natural language text and new forms of queries to retrieval systems. The challenge here is to bridge the gap between processing texts in natural language and evaluating arguments expressed in some formal language with a corresponding formal semantics. The hypothesis underlying this proposal is that controlled natural language (CNL) can provide the necessary link for bridging this gap. CNL can serve as an intermediate representation of argumentative text. They have the look and feel of natural language and are thus close enough to natural language based argument processing. On the other hand, the CNL we will be using is one which also possesses a well-defined formal semantics and is thus amenable to formal evaluation methods. CNLs of the kind mentioned already exist. A prominent example is Attempto Controlled English (ACE). ACE is easily understood by humans. At the same time, its semantics is formally defined based on a translation into discourse representation structures, which can be further translated into predicate logic. Since ACE was not primarily developed with argumentation in mind, we will thoroughly investigate whether the language constructs provided are sufficient for our purposes, or whether we need to extend the language. Within the project, we investigate argumentation in public, political discourse represented in newspaper text and related user comments with respect to five selected, controversial issues (e.g. free trade agreements). By modeling specific, empiric issues as CNL statements in a knowledge base, we extend existing approaches of argument mining from identification of generic, functional types of argument structures (e.g. premise, claim) towards semantic argument constituents. At the same time, representing such constituents as formally evaluable statements in a knowledge base allows for combination with approaches of abstract argumentation evaluation. We expect this to be a suitable framework to investigate specific argument structures as they generally occur in empiric communication (e.g. incomplete or inconsistent arguments).