Humor Detection via an Internal and External Neural Network

Abstract Humor as a lubricant for daily life is frequently used in language expressions. It is usually triggered by comparisons of metaphorical scenes or misunderstandings of ambiguous words. Hence, detecting and recognizing the humor implied in text is an interesting and challenging research problem in the field of natural language processing. To understand humor, we discuss incongruity and ambiguity in detail and then propose an internal and external attention neural network (IEANN) for the humor detection task. The IEANN integrates two types of attention mechanisms to capture the incongruity and ambiguity in humor text. Meanwhile, extensive experiments are conducted on two humor datasets to test the effectiveness and robustness of our model. The experimental results show that the proposed model not only achieves state-of-the-art performance but also has better interpretability.