  Humour detection from sentences has been an interesting and challenging task
in the last few years. In attempts to highlight humour detection, most research
was conducted using traditional approaches of embedding, e.g., Word2Vec or
Glove. Recently BERT sentence embedding has also been used for this task. In
this paper, we propose a framework for humour detection in short texts taken
from news headlines. Our proposed framework (IBEN) attempts to extract
information from written text via the use of different layers of BERT. After
several trials, weights were assigned to different layers of the BERT model.
The extracted information was then sent to a Bi-GRU neural network as an
embedding matrix. We utilized the properties of some external embedding models.
A multi-kernel convolution in our neural network was also employed to extract
higher-level sentence representations. This framework performed very well on
the task of humour detection.
