Speaker Attribution in G erman Parliamentary Debates with QL o RA -adapted Large Language Models (2024)

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.

## introduction language is central to the study of politics, as it forms the basis for political speech and debates @xcite . these textual sources offer rich insights into political dynamics and ideologies, yet the analysis of even moderately sized collections has been impeded by prohibitive costs. recent innovations from natural language processing (nlp) have the potential to significantly reduce the financial burden of scrutinizing extensive text corpora @xcite . this development coincides with the availability of a growing body of political texts, including german parliamentary data @xcite @xcite @xcite , thus opening new avenues for political research. political texts are usually unstructured, presenting challenges for automated analyses. an approach towards this challenge is automated speaker attribution @xcite , which detects who said what to whom in a speech event. this process involves detecting cue words that initiate a speech event and discerning the different roles (e.g., source, message, and addressee) associated with each event. this task is closely related to semantic role labeling (srl) that delineates the specific semantic relationships among a predicate and its corresponding arguments, such as "who" did "what" to "whom", "where", "when", and "why" @xcite . semantic role labeling is considered a key component for natural language understanding and has been demonstrated to enhance systems for various applications including question answering, machine translation, and video understanding @xcite . ## data and tasks the dataset of the germeval 2023 shared task on speaker attribution in german news articles and parliamentary debates consisted of 267 speeches from the german bundestag @xcite . this dataset included speeches from all seven parliamentary groups (including independent members of parliament as a separate group) of the 19th legislative period of the german bundestag (see human annotators followed annotation guidelines foot_1 to assign none, one, or multiple annotations to each sample. these annotations consisted of cue words that invoke speech events and roles (addr, evidence, medium, message, source, topic, ptc) associated with that event. while the cue is mandatory for each annotation, roles are context-dependent and may be absent. figure 1 shows example annotations. the shared task consisted of two subtasks: full annotation (subtask 1) and role detection (subtask 2) @xcite . in the full annotation subtask, the goal was to predict all cues and roles for each sample. in the role detection subtask, the gold cues were given, and the goal was to predict only the roles for each sample. the dataset was provided as five sets, namely trial, train, dev, and two eval sets (see ## conclusion we demonstrated that fine-tuned llama 2 language models can successfully predict cues and roles in german parliamentary debates, achieving competitive performance on the germeval2023 shared task without relying on traditional linguistic features. these results highlight the feasibility of automated speaker attribution by fine-tuning models on prompt templates that task them with identifying cues and roles. the similarity between automated speaker attribution and semantic role labeling suggests that this strategy may pave the way for new state-of-the-art results in various semantic role labeling tasks.