The construct of morality permeates our entire lives and influences our behavior and how we perceive others.It therefore comes at no surprise that morality also plays an important role in politics, as morally framed arguments are perceived as more appealing and persuasive.Thus, being able to identify moral framing in political communication and to detect subtle differences in politicians' moral framing can provide the basis for many interesting analyses in the political sciences.In the paper, we release MoralFramingInPolitics (MFiP), a new corpus of German parliamentary debates where the speakers' moral framing has been coded, using the framework of Moral Foundations Theory (MFT).Our fine-grained annotations distinguish different types of moral frames and also include narrative roles, together with the moral foundations for each frame.We then present models for frame type and moral foundation classification and explore the benefits of data augmentation (DA) and contrastive learning (CL) for the two tasks.All data and code will be made available to the research community.

This paper presents a new perspective on framing through the lens of speech acts and investigates how politicians make use of different pragmatic speech act functions in political debates. To that end, we created a new resource of German parliamentary debates, annotated with fine-grained speech act types. Our hierarchical annotation scheme distinguishes between cooperation and conflict communication, further structured into six subtypes, such as informative, declarative or argumentative-critical speech acts, with 14 fine-grained classes at the lowest level. We present classification baselines on our new data and show that the fine-grained classes in our schema can be predicted with an avg. F1 of around 82.0%. We then use our classifier to analyse the use of speech acts in a large corpus of parliamentary debates over a time span from 2003–2023.

Sotaro Takeshita, Tommaso Green, Ines Reinig, Kai Eckert, Simone Ponzetto. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.