论文标题
修辞，逻辑和辩证法：自然语言处理中基于理论的论点质量评估
Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
论文作者
论文摘要
尽管在计算参数质量（AQ）上的工作主要侧重于评估整体水平，但研究人员同意，作者将从针对论证理论的各个方面的反馈中受益。但是，缺少基于理论的大规模理论语料库和相应的计算模型。我们通过进行广泛的分析来填补这一差距，涵盖在线争论性写作的三个不同领域，并介绍Gaqcorpus：第一个大规模的英语多域（社区Q＆A论坛，辩论论坛，评论论坛，评论论坛），以基于理论的AQ分数注释。然后，我们提出了第一种基于理论评估的计算方法，该方法可以作为未来工作的强大基准。我们证明了大规模AQ注释的可行性，表明利用维度之间的关系会提高性能，并探索基于理论的预测与实践AQ评估之间的协同作用。
Though preceding work in computational argument quality (AQ) mostly focuses on assessing overall AQ, researchers agree that writers would benefit from feedback targeting individual dimensions of argumentation theory. However, a large-scale theory-based corpus and corresponding computational models are missing. We fill this gap by conducting an extensive analysis covering three diverse domains of online argumentative writing and presenting GAQCorpus: the first large-scale English multi-domain (community Q&A forums, debate forums, review forums) corpus annotated with theory-based AQ scores. We then propose the first computational approaches to theory-based assessment, which can serve as strong baselines for future work. We demonstrate the feasibility of large-scale AQ annotation, show that exploiting relations between dimensions yields performance improvements, and explore the synergies between theory-based prediction and practical AQ assessment.
