Taboo language across the globe: A multi-lab study.

The use of taboo words represents one of the most common and arguably universal linguistic behaviors, fulfilling a wide range of psychological and social functions. However, in the scientific literature, taboo language is poorly characterized, and how it is realized in different languages and populations remains largely unexplored. Here we provide a database of taboo words, collected from different linguistic communities (Study 1, N = 1046), along with their speaker-centered semantic characterization (Study 2, N = 455 for each of six rating dimensions), covering 13 languages and 17 countries from all five permanently inhabited continents. Our results show that, in all languages, taboo words are mainly characterized by extremely low valence and high arousal, and very low written frequency. However, a significant amount of cross-country variability in words' tabooness and offensiveness proves the importance of community-specific sociocultural knowledge in the study of taboo language.

Health-Related Content in Transformer-Based Language Models: Exploring Bias in Domain General vs. Domain Specific Training Sets.

In this communication, we demonstrate that the bias observed in domain general training sets with health-related content is not improved in domain specific health-communication corpora, contra.

Health-Related Content in Transformer-Based Deep Neural Network Language Models: Exploring Cross-Linguistic Syntactic Bias.

This paper explores a methodology for bias quantification in transformer-based deep neural network language models for Chinese, English, and French. When queried with health-related mythbusters on COVID-19, we observe a bias that is not of a semantic/encyclopaedical knowledge nature, but rather a syntactic one, as predicted by theoretical insights of structural complexity. Our results highlight the need for the creation of health-communication corpora as training sets for deep learning.

Could Linguistic Complexity Be Automatically Evaluated? A Multilingual Study on WHO's Emergency Learning Platform.

The ability of assessing any type of linguistic complexity of any given contents could potentially improve knowledge reproduction, especially tacit knowledge which can be expensive during a pandemic. In this paper, we develop a simple and crosslinguistic model of complexity which considers formal accounts on the study of linguistic systems, but can be easily implemented by non-linguists' groups, e.g., communication experts and policymakers. To test our model, we conduct a study on a corpus extracted from the World Health Organization (WHO)'s emergency learning platform in 6 languages. Data extracted from open-access encyclopaedic entries act as control groups. The results show that the measurements adopted signal a trend for a minimization of complexity and can be exploited as features for (automatic) text classification.

Evaluating Complexity of Digital Learning in a Multilingual Context: A Cross-Linguistic Study on WHO's Emergency Learning Platform.

Reproduction of knowledge, especially tacit knowledge can be expensive during a pandemic. One of the most common causes is the reduced information accessibility during the translation process. Having the ability to assess the linguistic complexity of any given contents could potentially improve knowledge reproduction. Authors conduct two cross-linguistic studies on the World Health Organization (WHO)'s emergency learning platform to assess the linguistic complexity of two online courses in 10 languages. Morpho-syntactically annotated treebanks, unannotated materials from Wikipedia and language-specific corpora are set as control groups. Preliminary findings reveal a clear reduced complexity of learning contents in the most candidate languages while retaining the maximum amount of information. Creating a baseline study on low-resourced languages on the learning genre could be potentially useful for measuring impact of normative products at country and local level.