“The Boating Store Had Its Best Sail Ever”: Pronunciation-attentive Contextualized Pun Recognition (2020)

Abstract: 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.

## introduction during the last decades, social media has promoted the creation of a vast amount of humorous web contents @xcite . automatic recognition of humor has become an important task in the area of figurative language processing, which can benefit various downstream nlp applications such as dialogue systems, sentiment analysis, and machine translation @xcite @xcite @xcite . however, humor is one of the most complicated behaviors in natural language semantics and sometimes it is even difficult for humans to interpret. in most cases, understanding humor requires adequate background knowledge and a rich context. homographic puns 1. did you hear about the guy whose whole left side was cut off? he's all right now. 2. i'd tell you a chemistry joke but i know i wouldn't get a reaction. heterographic puns 1. the boating store had its best sail (sale) ever. 2. i lift weights only on saturday and sunday because monday to friday are weak (week) days. puns are a form of humorous approaches using the different meanings of identical words or words with similar pronunciations to explain texts or utterances. there are two main types of puns. homographic puns rely on multiple interpretations of the same word. as shown in these two forms of puns have been studied in literature from different angles. to recognize puns in a sentence, word sense disambiguation techniques (wsd) @xcite have been employed to identify the equitable intention of words in utterances @xcite . external knowledge bases such as wordnet @xcite have been applied in determining word senses of pun words @xcite . however, these methods cannot tackle heterographic puns with distinct word spellings and knowledge bases that only contain a limited vocabulary. to resolve the issues of sparseness and heterographics, the word embedding techniques @xcite provide flexible representations to model puns @xcite @xcite . however, a word may have different meanings regarding its contexts. especially, an infrequent meaning of the word might be utilized for creating a pun. therefore, static word embeddings are insufficient to represent words. in addition, some puns are created by replacing a word with another word with the same or similar pronunciation as examples shown in in this paper, we propose pronunciation-attentive contextualized pun recognition (pcpr) to jointly model the contextualized word embeddings and phonological word representations for pun recognition. to capture the phonological structures of words, we break each word into a sequence of phonemes as its pronunciation so that homophones can have similar phoneme sets. for instance, the phonemes of the word pun are {p, ah, n}. in pcpr, we construct a pronunciation attentive module to identify important phonemes of each word, which can be applied in other tasks related to phonology. we jointly encode the contextual and phonological features into a self-attentive embedding to tackle both pun detection and location tasks. we summarize our contributions as following. ## pronunciation-attentive contextualized pun recognition in this section, we first formally define the problem and then introduce the proposed method, pcpr. ## experiments in this section, we describe our experimental settings and explain the results and interpretations. we will verify some basic assumptions of this paper: ## conclusions in this paper, we propose a novel approach, pcpr, for pun detection and location by leveraging a contextualized word encoder and modeling phonemes as word pronunciations. moreover, we would love to apply the proposed model to other problems, such as general humor recognition, irony discovery, and sarcasm detection, as the future work.