TY - GEN
T1 - Pun generation with surprise
AU - He, He
AU - Peng, Nanyun
AU - Liang, Percy
N1 - Funding Information:
This work was supported by the DARPA CwC program under ISI prime contract no. W911NF-15-1-0543 and ARO prime contract no. W911NF-15-1-0462. We thank Abhinav Moudgil and Jus-tine Kao for sharing their data and results. We also thank members of the Stanford NLP group and USC Plus Lab for insightful discussions.
Publisher Copyright:
© 2019 Association for Computational Linguistics
PY - 2019
Y1 - 2019
N2 - We tackle the problem of generating a pun sentence given a pair of homophones (e.g., “died” and “dyed”). Supervised text generation is inappropriate due to the lack of a large corpus of puns, and even if such a corpus existed, mimicry is at odds with generating novel content. In this paper, we propose an unsupervised approach to pun generation using a corpus of unhumorous text and what we call the local-global surprisal principle: we posit that in a pun sentence, there is a strong association between the pun word (e.g., “dyed”) and the distant context, as well as a strong association between the alternative word (e.g., “died”) and the immediate context. This contrast creates surprise and thus humor. We instantiate this principle for pun generation in two ways: (i) as a measure based on the ratio of probabilities under a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Human evaluation shows that our retrieve-and-edit approach generates puns successfully 31% of the time, tripling the success rate of a neural generation baseline.
AB - We tackle the problem of generating a pun sentence given a pair of homophones (e.g., “died” and “dyed”). Supervised text generation is inappropriate due to the lack of a large corpus of puns, and even if such a corpus existed, mimicry is at odds with generating novel content. In this paper, we propose an unsupervised approach to pun generation using a corpus of unhumorous text and what we call the local-global surprisal principle: we posit that in a pun sentence, there is a strong association between the pun word (e.g., “dyed”) and the distant context, as well as a strong association between the alternative word (e.g., “died”) and the immediate context. This contrast creates surprise and thus humor. We instantiate this principle for pun generation in two ways: (i) as a measure based on the ratio of probabilities under a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Human evaluation shows that our retrieve-and-edit approach generates puns successfully 31% of the time, tripling the success rate of a neural generation baseline.
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M3 - Conference contribution
AN - SCOPUS:85084185277
T3 - NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
SP - 1734
EP - 1744
BT - Long and Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Y2 - 2 June 2019 through 7 June 2019
ER -