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

Integrating extracted information from bert and multiple embedding methods with the deep neural network for humour detection