University of Zurich
Ada Wan
fairness;evaluation;multilingual NLP / multilinguality;representation learning for language data;statistical comparisons;Double Descent;conditional language modeling;data-centric approach;diversity in AI;morphology;Transformer;meta evaluation;visualization or interpretation of learned representations;character encoding;internationalization and localization;robustness;statistical science for NLP;science in the era of AI/DL (AIxScience);transdisciplinarity
Fairness in Representation for Multilingual NLP: Insights from Controlled Experiments on Conditional Language Modeling