TY  - JOUR
A1  - Bornheim, Tobias
A1  - Grieger, Niklas
A1  - Blaneck, Patrick Gustav
A1  - Bialonski, Stephan
T1  - Speaker Attribution in German Parliamentary Debates with QLoRA-adapted Large Language Models
T2  - Journal for language technology and computational linguistics : JLCL
N2  - 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.
KW  - large language models
KW  - German
KW  - speaker attribution
KW  - semantic role labeling
Y1  - 2024
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/11053
SN  - 2190-6858
VL  - 37
IS  - 1
PB  - Gesellschaft für Sprachtechnologie und Computerlinguistik
CY  - Regensburg
ER  -