The lack of annotated data on professional argumentation and complete argumentative debates has led to the oversimplification and the inability of approaching more complex natural language processing tasks. Such is the case of the automatic debate evaluation. In this paper, we propose an original hybrid method to automatically evaluate argumentative debates. For that purpose, we combine concepts from argumentation theory such as argumentation frameworks and semantics, with Transformer-based architectures and neural graph networks. Furthermore, we obtain promising results that lay the basis on an unexplored new instance of the automatic analysis of natural language arguments.

翻译：专业论证及完整论证性辩论的标注数据匮乏，导致相关自然语言处理任务被过度简化且难以处理更复杂的场景，自动辩论评估便是典型案例。本文提出一种原创性混合方法，用于自动评估论证性辩论。为此，我们融合了论证理论中的论证框架与语义等概念，结合基于Transformer的架构与神经图网络。实验取得了具有前景的结果，为自然语言论证自动分析这一尚未被充分探索的新方向奠定了基础。