Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model's flexibility by finetuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.

Previous research on predictors of populism has predominantly focused on socio-economic (e.g., education, employment, social status), and socio-cultural factors (e.g., social identity and social status). However, during the last years, the role of negative emotions has become increasingly prominent in the study of populism. We conducted a cross-national survey in 15 European countries (N=8059), measuring emotions towards the government and the elites, perceptions of threats about the future, and socio-economic factors as predictors of populist attitudes (the latter operationalized via three existing scales, anti-elitism, Manichaean outlook, people-centrism, and a newly developed scale on nativism). We tested the role of emotional factors in a deductive research design based on a structural model. Our results show that negative emotions (anger, contempt and anxiety) are better predictors of populist attitudes than mere socio-economic and socio-cultural factors. An inductive machine learning algorithm, Random Forest (RF), reaffirmed the importance of emotions across our survey dataset.

There has been an increased interest in modelling political discourse within the natural language processing (NLP) community, in tasks such as political bias and misinformation detection, among others. Metaphor-rich and emotion-eliciting communication strategies are ubiquitous in political rhetoric, according to social science research. Yet, none of the existing computational models of political discourse has incorporated these phenomena. In this paper, we present the first joint models of metaphor, emotion and political rhetoric, and demonstrate that they advance performance in three tasks: predicting political perspective of news articles, party affiliation of politicians and framing of policy issues.

Computational modelling of political discourse tasks has become an increasingly important area of research in natural language processing. Populist rhetoric has risen across the political sphere in recent years; however, computational approaches to it have been scarce due to its complex nature. In this paper, we present the new Us vs. Them dataset, consisting of 6861 Reddit comments annotated for populist attitudes and the first large-scale computational models of this phenomenon. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We set a baseline for two tasks related to populist attitudes and present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.