PhD defence by Rasmus Kær Jørgensen
Summary
This thesis presents research that investigates multilingual NLP for applications in the financial domain. Accounting firms need a system that autonomously learns to handle these transactions accurately, even from limited training data. The second line of research advances multilingual NLP in the financial domain by extending domain-adaptive pretraining to a multilingual scenario, focusing on adapting a single model to multiple languages within a specific domain. Several domain-specific resources for model evaluation are proposed, including a financial benchmark covering multiple languages for evaluating multilingual financial language models. The third part evaluates the explanations produced by interpretation methods for multilingual NLP systems.