Abstract
Humor refers to the quality of being amusing. With the development of artificial intelligence, humor recognition is attracting a lot of research attention. Although phonetics and ambiguity have been introduced by previous studies, existing recognition methods still lack suitable feature design for neural networks. In this paper, we illustrate that phonetics structure and ambiguity associated with confusing words need to be learned for their own representations via the neural network. Then, we propose the Phonetics and Ambiguity Comprehension Gated Attention network (PACGA) to learn phonetic structures and semantic representation for humor recognition. The PACGA model can well represent phonetic information and semantic information with ambiguous words, which is of great benefit to humor recognition. Experimental results on two public datasets demonstrate the effectiveness of our model.
Highlights
Humor is frequently used in daily communication [1]
We propose an end-to-end neural network named Phonetics and Ambiguity Comprehension Gated Attention network to detect humor in text. e proposed model captures the phonetic information by Convolutional Neural Networks (CNN), combines with Bidirectional Gated Recurrent Units (Bi-GRU) and attention mechanism to build the information of context and ambiguous words, and applies gated mechanism to adjust the effects of the two kinds of information in the task of humor recognition
Our work makes three contributions: (1) For solving phonetic structure and ambiguity features in humor recognition, we propose a novel framework named Phonetics and Ambiguity Comprehension Gated Attention network (PACGA), which can understand the phonetic representation by the CNN model, and learn latent semantic representation associated with ambiguous words by BiGRU and attention mechanism
Summary
Humor is frequently used in daily communication [1]. When interacting with people, if artificial intelligence (AI) systems, such as chatbots, can detect humor within the conversation, it will help them better understand the emotions of the human and help the AI make more appropriate decisions.erefore, humor computation deserves particular attention, as it has the potential to turn computers into creative and motivational tools for human activity [2].Humor recognition refers to determining whether a sentence in a given context expresses a certain degree of humor. Humor is frequently used in daily communication [1]. If artificial intelligence (AI) systems, such as chatbots, can detect humor within the conversation, it will help them better understand the emotions of the human and help the AI make more appropriate decisions. Erefore, humor computation deserves particular attention, as it has the potential to turn computers into creative and motivational tools for human activity [2]. Yang et al [3] identified three semantic structures and a phonetic structure behind humor. Experimental results show that ambiguity and phonetic structures are important for humor recognition. Phonetic structures, used as devices in humorous texts, usually take the form of alliteration or rhyme. Alliteration, rhyme, or word repetition are often used to evoke or enhance the effect of humor even if the content is not humorous
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