  Automation of humor detection and rating has interesting use cases in modern
technologies, such as humanoid robots, chatbots, and virtual assistants. In
this paper, we propose a novel approach for detecting and rating humor in short
texts based on a popular linguistic theory of humor. The proposed technical
method initiates by separating sentences of the given text and utilizing the
BERT model to generate embeddings for each one. The embeddings are fed to
separate lines of hidden layers in a neural network (one line for each
sentence) to extract latent features. At last, the parallel lines are
concatenated to determine the congruity and other relationships between the
sentences and predict the target value. We accompany the paper with a novel
dataset for humor detection consisting of 200,000 formal short texts. In
addition to evaluating our work on the novel dataset, we participated in a live
machine learning competition focused on rating humor in Spanish tweets. The
proposed model obtained F1 scores of 0.982 and 0.869 in the humor detection
experiments which outperform general and state-of-the-art models. The
evaluation performed on two contrasting settings confirm the strength and
robustness of the model and suggests two important factors in achieving high
accuracy in the current task: 1) usage of sentence embeddings and 2) utilizing
the linguistic structure of humor in designing the proposed model.
