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 .
 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 .
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Argumentative quality is an important feature of everyday writing in many textual domains, such as online reviews and question-and-answer (Q
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, 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 --
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corpus and
theory-based corpus and corresponding
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In this work, we advance theory-based argument quality research
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and presenting GAQCorpus: the first large-scale English multi-domain (community
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and review forums. We start with an annotation study with linguistic experts and crowd workers, resulting in the first large-scale English
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argument quality scores, dubbed AQCorpus. Next, we
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argument quality
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Our research yields interesting findings including
We demonstrate
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theory-based argument quality annotations, the fact that relations between theory-based argument quality dimensions can be exploited to yield
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demonstrates the usefulness of
explore the synergies between
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argument quality predictions with respect to the
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view