Overconfidence in radical politics

Across the world, radical and populist political movements have done well electorally. Such radical movements may be politically left- or right-extreme, and some populist movements can be quite radical without necessarily being at the edges of the political spectrum. What do followers of these different radical movements have in common psychologically? The present chapter will review findings indicating that people hold radical political beliefs with high confidence. Research specifically suggests that radical beliefs are rooted in psychological distress, leading people to embrace a relatively simplistic worldview that offers them epistemic clarity. This worldview is, for instance, manifested in the belief that there are simple solutions for complex societal problems, and in an oversimplified perception of the social world as reflected in stereotyping. Epistemic clarity hence promotes confidence by fueling the belief that one understands reality. But while confidence sometimes may be rooted in actual knowledge or expertise, empirical findings indicate that this is generally not the case for people holding radical political beliefs. For instance, political extremists felt more certain of their knowledge of the EU refugee crisis yet did not score better than moderates on a factual knowledge test. Moreover, in a Dutch referendum about a political treaty, anti-establishment voting (a core feature of populism) was associated with increased self-perceived understanding of the treaty but also with decreased actual knowledge, and with an increased general tendency to overclaim knowledge. Apparently, the high levels of confidence displayed in radical and populist rhetoric is often overconfidence

Delexicalized Word Embeddings for Cross-lingual Dependency Parsing

International audienceThis paper presents a new approach to the problem of cross-lingual dependency parsing, aiming at leveraging training data from different source languages to learn a parser in a target language. Specifically , this approach first constructs word vector representations that exploit structural (i.e., dependency-based) contexts but only considering the morpho-syntactic information associated with each word and its contexts. These delexicalized word em-beddings, which can be trained on any set of languages and capture features shared across languages, are then used in combination with standard language-specific features to train a lexicalized parser in the target language. We evaluate our approach through experiments on a set of eight different languages that are part the Universal Dependencies Project. Our main results show that using such delexicalized embeddings, either trained in a monolin-gual or multilingual fashion, achieves significant improvements over monolingual baselines