  Designing predictive models for subjective problems in natural language
processing (NLP) remains challenging. This is mainly due to its
non-deterministic nature and different perceptions of the content by different
humans. It may be solved by Personalized Natural Language Processing (PNLP),
where the model exploits additional information about the reader to make more
accurate predictions. However, current approaches require complete information
about the recipients to be straight embedded. Besides, the recent methods focus
on deterministic inference or simple frequency-based estimations of the
probabilities. In this work, we overcome this limitation by proposing a novel
approach to capture the uncertainty of the forecast using conditional
Normalizing Flows. This allows us to model complex multimodal distributions and
to compare various models using negative log-likelihood (NLL). In addition, the
new solution allows for various interpretations of possible reader perception
thanks to the available sampling function. We validated our method on three
challenging, subjective NLP tasks, including emotion recognition and hate
speech. The comparative analysis of generalized and personalized approaches
revealed that our personalized solutions significantly outperform the baseline
and provide more precise uncertainty estimates. The impact on the text
interpretability and uncertainty studies are presented as well. The information
brought by the developed methods makes it possible to build hybrid models whose
effectiveness surpasses classic solutions. In addition, an analysis and
visualization of the probabilities of the given decisions for texts with high
entropy of annotations and annotators with mixed views were carried out.
