We present ASR Bundestag, a dataset for automatic speech recognition in German, consisting of 610 hours of aligned audio-transcript pairs for supervised training as well as 1,038 hours of unlabeled audio snippets for self-supervised learning, based on raw audio data and transcriptions from plenary sessions and committee meetings of the German parliament. In addition, we discuss utilized approaches for the automated creation of speech datasets and assess the quality of the resulting dataset based on evaluations and finetuning of a pre-trained state of the art model. We make the dataset publicly available, including all subsets.
PopBERT. Detecting populism and its host ideologies in the German Bundestag
University of Stuttgart（斯图加特大学）；Institute for Social Sciences, University of Stuttgart（斯图加特大学社会科学研究所）；Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）
University of Mannheim（曼海姆大学）；Technische Universität Darmstadt（达姆施塔特工业大学）
We analyze bias in historical corpora as encoded in diachronic distributional semantic models by focusing on two specific forms of bias, namely a political (i.e., anti-communism) and racist (i.e., antisemitism) one. For this, we use a new corpus of German parliamentary proceedings, DeuPARL, spanning the period 1867--2020. We complement this analysis of historical biases in diachronic word embeddings with a novel measure of bias on the basis of term co-occurrences and graph-based label propagation. The results of our bias measurements align with commonly perceived historical trends of antisemit…
Early detection of relevant locations in a piece of news is especially important in extreme events such as environmental disasters, war conflicts, disease outbreaks, or political turmoils. Additionally, this detection also helps recommender systems to promote relevant news based on user locations. Note that, when the relevant locations are not mentioned explicitly in the text, state-of-the-art methods typically fail to recognize them because these methods rely on syntactic recognition. In contrast, by incorporating a knowledge base and connecting entities with their locations, our system succe…
University of Bremen（不来梅大学）；University of Stuttgart（斯图加特大学）
Newspaper reports provide a rich source of information on the unfolding of public debate on specific policy fields that can serve as basis for inquiry in political science. Such debates are often triggered by critical events, which attract public attention and incite the reactions of political actors: crisis sparks the debate. However, due to the challenges of reliable annotation and modeling, few large-scale datasets with high-quality annotation are available. This paper introduces DebateNet2.0, which traces the political discourse on the European refugee crisis in the German quality newspape…
University of Göttingen（哥廷根大学）；Max Planck Institute for Empirical Aesthetics（马克斯·普朗克经验美学研究所）
Automated text analysis has become a widely used tool in political science. In this research, we use a BERT model trained on German party manifestos to identify the individual parties' contribution to the coalition agreement of 2021.
University of Hildesheim（希尔德斯海姆大学）；University of Antwerp（安特卫普大学）
Discussion about the social network Twitter often concerns its role in political discourse, involving the question of when an expression of opinion becomes offensive, immoral, and/or illegal, and how to deal with it. Given the growing amount of offensive communication on the internet, there is a demand for new technology that can automatically detect hate speech, to assist content moderation by humans. This comes with new challenges, such as defining exactly what is free speech and what is illegal in a specific country, and knowing exactly what the linguistic characteristics of hate speech are…
Gender-inclusive language is important for achieving gender equality in languages with gender inflections, such as German. While stirring some controversy, it is increasingly adopted by companies and political institutions. A handful of tools have been developed to help people use gender-inclusive language by identifying instances of the generic masculine and providing suggestions for more inclusive reformulations. In this report, we define the underlying tasks in terms of natural language processing, and present a dataset and measures for benchmarking them. We also present a model that implem…
Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debates
Bielefeld University（比勒费尔德大学）；TU Darmstadt（达姆施塔特工业大学）；Hans-Bredow-Institut（汉斯·布雷多研究所）；University of Mannheim（曼海姆大学）；University of Technology Nuremberg（纽伦堡工业大学）
Do We Still Need Humans in the Loop? Human vs. LLM Annotation in Active Learning for TikTok Hate Speech Detection
Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）；Department of Computer Science, University of Copenhagen（哥本哈根大学计算机科学系）；Munich Center for Machine Learning（慕尼黑机器学习中心）
