@incollection{EngelmannBaumann2023,
  author    = {Engelmann, Ulrich M. and Baumann, Martin},
  title     = {Moderationsexpertise f{\"u}r QMBs - das Mindset},
  series = {Qualit{\"a}tsmanagement in Dienstleistungsunternehmen},
  booktitle = {Qualit{\"a}tsmanagement in Dienstleistungsunternehmen},
  editor    = {Thomann, Hermann and Tr{\"a}ger, Thomas},
  publisher = {T{\"U}V-Verlag},
  address   = {K{\"o}ln},
  isbn      = {978-3-8249-0473-0},
  pages     = {Kapitel 08630},
  year      = {2023},
  abstract  = {Teamsitzungen, Arbeitsgruppentreffen, Kickoffs und Meetings - sie alle werden mit dem Ziel durchgef{\"u}hrt, innerhalb einer vorgegebenen Zeitspanne ein gemeinsames Arbeitsziel zu erreichen. Damit die Zielerreichung auch bei komplexeren Arbeitsauftr{\"a}gen nicht vom Zufall abh{\"a}ngt, empfiehlt es sich, die Leitung des Ablaufs einem Moderator zu {\"u}bertragen. In diesem Beitrag einer mehrteiligen Serie wird beschrieben, {\"u}ber welches Mindset der Moderator verf{\"u}gen sollte, welche grunds{\"a}tzlichen Methoden hilfreich sind und was bei der Onlinemoderation im Besonderen zu beachten ist.},
  language  = {de}
}
@unpublished{BornheimGriegerBlanecketal.2023,
  author    = {Bornheim, Tobias and Grieger, Niklas and Blaneck, Patrick Gustav and Bialonski, Stephan},
  title     = {Preprint: Speaker attribution in German parliamentary debates with QLoRA-adapted large language models},
  series = {Journal for Language Technology and Computational Linguistics},
  journal   = {Journal for Language Technology and Computational Linguistics},
  doi       = {10.48550/arXiv.2309.09902},
  pages     = {8 Seiten},
  year      = {2023},
  abstract  = {The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.},
  language  = {en}
}