Comparing Architectures for Supervised Political Scaling

Text scaling, the task of positioning political actors on an ideological scale, is a fundamental task in political analysis. To ease the need for manual analysis, various NLP methods have been proposed for this task, including classification- and regression-based approaches, showing successes as well as limitations. The goal of our paper is to consolidate the state of the art in this area. We ask two questions: (a) Can the performance of scaling methods be improved by predicting scales not individually but jointly? (b) Is there a middle ground between classification and regression?

Modelling Adjectival Modification Effects on Semantic Plausibility

While the task of assessing the plausibility of events such as ''news is relevant'' has been addressed by a growing body of work, less attention has been paid to capturing changes in plausibility as triggered by event modification. Understanding changes in plausibility is relevant for tasks such as dialogue generation, commonsense reasoning, and hallucination detection as it allows to correctly model, for example, ''gentle sarcasm'' as a sign of closeness rather than unkindness among friends [9]. In this work, we tackle the ADEPT challenge benchmark [6] consisting of 16K English sentence pairs differing by exactly one adjectival modifier. Our modeling experiments provide a conceptually novel method by using sentence transformers, and reveal that both they and transformer-based models struggle with the task at hand, and sentence transformers - despite their conceptual alignment with the task - even under-perform in comparison to models like RoBERTa. Furthermore, an in-depth comparison with prior work highlights the importance of a more realistic, balanced evaluation method: imbalances distort model performance and evaluation metrics, and weaken result trustworthiness.