A pun is a form of wordplay for an intended humorous or rhetorical effect , where a word suggests two or more meanings by exploiting polysemy ( homographic pun ) or phonological similarity to another word ( heterographic pun ) . This paper presents an approach that addresses pun detection and pun location jointly from a sequence labeling perspective . We employ a new tagging scheme such that the model is capable of performing such a joint task , where useful structural information can be properly captured . We show that our proposed model is effective in handling both homographic and heterographic puns . Empirical results on the benchmark datasets demonstrate that our approach can achieve new state-ofthe-art results . There exists a class of language construction known as pun in natural language texts and utterances , where a certain word or other lexical items are used to exploit two or more separate meanings . It has been shown that understanding of puns is an important research question with various real-world applications , such as human-computer interaction ( Morkes et al . , 1999 ; Hempelmann , 2008 ) and machine translation ( Schröter , 2005 ) . Recently , many researchers show their interests in studying puns , like detecting pun sentences ( Vadehra , 2017 ) , locating puns in the text ( Cai et al . , 2018 ) , interpreting pun sentences ( Sevgili et al . , 2017 ) and generating sentences containing puns ( Ritchie , 2005 ; Hong and Ong , 2009 ; Yu et al . , 2018 ) . A pun is a wordplay in which a certain word suggests two or more meanings by exploiting polysemy , homonymy , or phonological similarity to another sign , for an intended humorous or rhetorical effect . Puns can be generally categorized into two groups , namely hetero-graphic puns ( where the pun and its latent target are phonologically similar ) and homographic puns ( where the two meanings of the pun reflect its two distinct senses ) ( Miller et al . , 2017 ) . Consider the following two examples : ( 1 ) When the church bought gas for their annual barbecue , proceeds went from the sacred to the propane . ( 2 ) Some diets cause a gut reaction . The first punning joke exploits the sound similarity between the word " propane " and the latent target " profane " , which can be categorized into the group of heterographic puns . Another categorization of English puns is homographic pun , exemplified by the second instance leveraging distinct senses of the word " gut " . Pun detection is the task of detecting whether there is a pun residing in the given text . The goal of pun location is to find the exact word appearing in the text that implies more than one meanings . Most previous work addresses such two tasks separately and develop separate systems ( Pramanick and Das , 2017 ; Sevgili et al . , 2017 ) . Typically , a system for pun detection is built to make a binary prediction on whether a sentence contains a pun or not , where all instances ( with or without puns ) are taken into account during training . For the task of pun location , a separate system is used to make a single prediction as to which word in the given sentence in the text that trigger more than one semantic interpretations of the text , where the training data involves only sentences that contain a pun . Therefore , if one is interested in solving both problems at the same time , a pipeline approach that performs pun detection followed by pun location can be used . Compared to the pipeline methods , joint learning has been shown effective ( Katiyar and Cardie , 2016 ; Peng et al . , 2018 ) since it is able to re-duce error propagation and allows information exchange between tasks which is potentially beneficial to all the tasks . In this work , we demonstrate that the detection and location of puns can be jointly addressed by a single model . The pun detection and location tasks can be combined as a sequence labeling problem , which allows us to jointly detect and locate a pun in a sentence by assigning each word a tag . Since each context contains a maximum of one pun ( Miller et al . , 2017 ) , we design a novel tagging scheme to capture this structural constraint . Statistics on the corpora also show that a pun tends to appear in the second half of a context . To capture such a structural property , we also incorporate word position knowledge into our structured prediction model . Experiments on the benchmark datasets show that detection and location tasks can reinforce each other , leading to new state-of-the-art performance on these two tasks . To the best of our knowledge , this is the first work that performs joint detection and location of English puns by using a sequence labeling approach.1 2 Approach In this paper , we propose to perform pun detection and location tasks in a joint manner from a sequence labeling perspective . We observe that each text in our corpora contains a maximum of one pun . Hence , we design a novel tagging scheme to incorporate such a constraint . Such a scheme guarantees that there is a maximum of one word that will be tagged as a pun during the testing phase . We also found the interesting structural property such as the fact that most puns tend to appear at the second half of the sentences can be helpful for such a task , but was not explored in previous works . Furthermore , unlike many previous approaches , our approach , though simple , is generally applicable to both heterographic and homographic puns . Empirical results on the benchmark datasets prove the effectiveness of the proposed approach that the two tasks of pun detection and location can be addressed by a single model from a sequence labeling perspective . Future research includes the investigations on how to make use of richer semantic and linguistic information for detection and location of puns . Research on puns for other languages such as Chinese is still under-explored , which could also be an interesting direction for our future studies .

Challenge: Existing pun detection works take a pipeline approach that is composed of detection and location while joint learning approaches perform well on other NLP tasks.
Approach: They propose to approach pun detection and location jointly as sequence labelling using a new tagging scheme that exploits structure information.
Outcome: The proposed approach is effective for homographic and heterographic puns and achieves state-of-the-art performance and most puns appear in the second half of sentences.