Argumentative quality is an important feature of everyday writing in many textual domains, such as online reviews and question-and-answer (Q&A) forums. Authors can improve their writing with feedback targeting individual aspects of argument quality (AQ), even though preceding work has mostly focused on assessing the overall AQ. These individual aspects are reflected in theory-based dimensions of argument quality, but automatic assessment in real-world texts is still in its infancy -- a large-scale corpus and computational models are missing. In this work, we advance theory-based argument quality research by conducting an extensive analysis covering three diverse domains of online argumentative writing: Q&A forums, debate forums, and review forums. We start with an annotation study with linguistic experts and crowd workers, resulting in the first large-scale English corpus annotated with theory-based argument quality scores, dubbed AQCorpus. Next, we propose the first computational approaches to theory-based argument quality assessment, which can serve as strong baselines for future work. Our research yields interesting findings including the feasibility of large-scale theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield performance improvements, and demonstrates the usefulness of theory-based argument quality predictions with respect to the practical AQ assessment view.

本研究针对计算论证质量(AQ)的整体评估展开前期工作，提出了一种基于理论的评估方法，并开发了首个大规模的英文多领域(AQ社区问答、辩论、评论)语料库GAQCorpus，为之后的工作提供了坚实的基础。

修辞、逻辑和辩证法：自然语言处理中理论为基础的论证质量评估的推进