Introducing ADEA: a German dataset that captures online dialogues and focuses on ethical issues related to future AI applications. This dataset, which includes over 2800 labeled user utterances on four different topics, is specifically designed for the training of chatbots that can navigate the complexities of real-world ethical AI conversations. The creation of these dialogues is the result of two carefully conducted studies in which university students interacted with an argumentative dialogue system. A fundamental part of our methodology is the use of German argument graphs. These graphs not only form the knowledge base of the dialogue system but also serve as an effective annotation scheme for the dialogues. Apart from the introduction of the dataset and the argument graphs, we provide a preliminary benchmark using GPT-4 via the OpenAI API. This provides researchers with a concrete reference point while demonstrating the potential of our dataset. We make our dataset and argument graphs available at https://github.com/HaupChris/ADEA-Dialogue-Dataset.

This paper describes our system for the SemEval 2019 Task 4 on hyperpartisan news detection. We build on an existing deep learning approach for sentence classification based on a Convolutional Neural Network. Modifying the original model with additional layers to increase its expressiveness and finally building an ensemble of multiple versions of the model, we obtain an accuracy of 67.52% and an F1 score of 73.78% on the main test dataset. We also report on additional experiments incorporating handcrafted features into the CNN and using it as a feature extractor for a linear SVM.