  Humor plays an important role in human languages and it is essential to model
humor when building intelligence systems. Among different forms of humor, puns
perform wordplay for humorous effects by employing words with double entendre
and high phonetic similarity. However, identifying and modeling puns are
challenging as puns usually involved implicit semantic or phonological tricks.
In this paper, we propose Pronunciation-attentive Contextualized Pun
Recognition (PCPR) to perceive human humor, detect if a sentence contains puns
and locate them in the sentence. PCPR derives contextualized representation for
each word in a sentence by capturing the association between the surrounding
context and its corresponding phonetic symbols. Extensive experiments are
conducted on two benchmark datasets. Results demonstrate that the proposed
approach significantly outperforms the state-of-the-art methods in pun
detection and location tasks. In-depth analyses verify the effectiveness and
robustness of PCPR.
