  Detecting if a text is humorous is a hard task to do computationally, as it
usually requires linguistic and common sense insights. In machine learning,
humor detection is usually modeled as a binary classification task, trained to
predict if the given text is a joke or another type of text. Rather than using
completely different non-humorous texts, we propose using text generation
algorithms for imitating the original joke dataset to increase the difficulty
for the learning algorithm. We constructed several different joke and non-joke
datasets to test the humor detection abilities of different language
technologies. In particular, we compare the humor detection capabilities of
classic neural network approaches with the state-of-the-art Dutch language
model RobBERT. In doing so, we create and compare the first Dutch humor
detection systems. We found that while other language models perform well when
the non-jokes came from completely different domains, RobBERT was the only one
that was able to distinguish jokes from generated negative examples. This
performance illustrates the usefulness of using text generation to create
negative datasets for humor recognition, and also shows that transformer models
are a large step forward in humor detection.
