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Speaker Attribution in German Parliamentary Debates with QLoRA-adapted Large Language Models

  • 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.

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Metadaten
Author:Tobias Bornheim, Niklas Grieger, Patrick Gustav Blaneck, Stephan BialonskiORCiD
DOI:https://doi.org/10.21248/jlcl.37.2024.244
ISSN:2190-6858
Parent Title (English):Journal for language technology and computational linguistics : JLCL
Publisher:Gesellschaft für Sprachtechnologie und Computerlinguistik
Place of publication:Regensburg
Document Type:Article
Language:English
Year of Completion:2024
Date of the Publication (Server):2024/03/05
Tag:German; large language models; semantic role labeling; speaker attribution
Volume:37
Issue:1
Length:13 Seiten
Link:https://doi.org/10.21248/jlcl.37.2024.244
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
Licence (German):License LogoCreative Commons - Namensnennung-Weitergabe unter gleichen Bedingungen