  The vast area of subjectivity in Natural Language Processing (NLP) poses a
challenge to the solutions typically used in generalized tasks. As exploration
in the scope of generalized NLP is much more advanced, it implies the
tremendous gap that is still to be addressed amongst all feasible tasks where
an opinion, taste, or feelings are inherent, thus creating a need for a
solution, where a data fusion could take place. We have chosen the task of
funniness, as it heavily relies on the sense of humor, which is fundamentally
subjective. Our experiments across five personalized and four generalized
datasets involving several personalized deep neural architectures have shown
that the task of humor detection greatly benefits from the inclusion of
personalized data in the training process. We tested five scenarios of training
data fusion that focused on either generalized (majority voting) or
personalized approaches to humor detection. The best results were obtained for
the setup, in which all available personalized datasets were joined to train
the personalized reasoning model. It boosted the prediction performance by up
to approximately 35% of the macro F1 score. Such a significant gain was
observed for all five personalized test sets. At the same time, the impact of
the model's architecture was much less than the personalization itself. It
seems that concatenating personalized datasets, even with the cost of
normalizing the range of annotations across all datasets, if combined with the
personalized models, results in an enormous increase in the performance of
humor detection.
