CUEPAQ: Visual Analytics and Linguistics for Capturing, Understanding, and Explaining Personalized Argument Quality

在我们的项目中，可视化分析和语言学捕捉，理解和解释个性化的论点质量（CUEPAQ），我们联合收割机方法从可视化分析和计算语言学领域产生新的方法来分析论点质量的各种指标在不同层次的语言分析。基于这种分析，我们提供了所谓的偏好配置文件，使用户能够深入了解他们的个人论证行为，以及将其与其他用户的行为进行比较。本项目的主要目标是捕捉，理解和解释论证的感知质量。为此，我们收集了各种文体，内容和语义特征，影响如何论点的框架和感知。这个项目的核心问题是这些元素如何相互作用，产生被认为是高质量的论点。在回答这个问题时，我们有助于对论点质量的研究，一个可视化的分析框架，用于对论点进行评级和排名。该系统能够快速分析论证质量和论证的语言表达之间的相互作用。我们的框架提取偏好配置文件，通过指示特别影响他们的参数评级的内容和风格特征来捕获用户的注释行为。这些偏好简档可能因用户而异，或者跨不同的用户组而异。为了解释这一点，我们包括专家知识的注释的论点质量，以及结果的非专家用户评级在我们的分析的论点质量。基于参数之间的相对偏好比较，该系统提取的语言特征，风格和解释模式。这些功能预计将捕捉用户的偏好，从而反映在他们的评级行为。这种外部化的知识是基于某些指导策略可视化的，并允许用户和系统相互学习。由于系统可以跟踪不同用户的注释行为，这种自适应过程不仅允许用户了解自己的论证偏好，而且还可以将其与系统的其他用户以及专家对高质量论证的意见进行比较。在这个项目中，我们使用计算语言学的方法来探索语言选择和用户和系统基于专家意见的参数排名之间的关系。具体地说，我们致力于对与论证质量判断相关的论证语言特征进行统一的诠释。

In our project, Visual Analytics and Linguistics for Capturing, Understanding, and Explaining Personalized Argument Quality (CUEPAQ) we combine methods from the fields of visual analytics and computational linguistic to generate new approaches for the analysis of argument quality in terms of various metrics on different levels of linguistic analysis. Based on this analysis, we provide, so-called, preference profiles, enabling users to gain insights into their personal argumentation behavior, as well as compare it to the behavior of other users.The main goal of this project is to capture, understand, and explain the perceived quality of arguments. To that end, we collect various stylistic, content, and semantic features that influence how arguments are framed and perceived. The central question of this project is how these elements interact to produce arguments that are perceived as high-quality.In answering this question, we contribute to the research on argument quality a visual analytics framework for the rating and ranking of arguments. The system enables rapid analysis of interactions between argument quality and the linguistic expression of an argument. Our framework extracts preference profiles, which capture the annotation behavior of users by indicating the content and stylistic features that particularly affect their rating of arguments. These preference profiles may vary from user to user, or across different user groups. To account for this, we include both expert knowledge on the annotation of argument quality, as well as results of non-expert user ratings in our analysis of argument quality. Based on relative preference comparisons between arguments, the system extracts patterns of linguistic features, both stylistic and interpretational. These features are expected to capture the users' preferences and would thus be reflected in their rating behavior. This externalized knowledge is visualized based on certain guidance strategies and allows both the user and the system to learn from each other. Since the system can keep track of the annotation behavior of different users, this co-adaptive process does not only allow a user to understand their own argumentation preferences but also to compare them with other users of the system, as well as the expert opinions on high-quality argumentation. In this project, we use computational linguistic methods to explore the relationship between linguistic choices and the ranking of arguments by users and systems based on expert-opinion. Concretely, we contribute to the uniform annotation of linguistic features of arguments that are relevant to the judgment of argument quality.