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        <title>Pun Generation with Surprise</title>
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    <abstract>We tackle the problem of generating a pun sentence given a pair of homophones (e.g., “died” and “dyed”). Puns are by their very nature statistically anomalous and not amenable to most text generation methods that are supervised by a large corpus. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Specifically, we posit that in a pun sentence, there is a strong association between the pun word (e.g., “dyed”) and the distant context, but a strong association between the alternative word (e.g., “died”) and the immediate context. We instantiate the surprisal principle in two ways: (i) as a measure based on the ratio of probabilities given by a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Based on human evaluation, our retrieve-and-edit approach generates puns successfully 30% of the time, doubling the success rate of a neural generation baseline.</abstract>
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%0 Conference Proceedings
%T Pun Generation with Surprise
%A He, He
%A Peng, Nanyun
%A Liang, Percy
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F he-etal-2019-pun
%X We tackle the problem of generating a pun sentence given a pair of homophones (e.g., “died” and “dyed”). Puns are by their very nature statistically anomalous and not amenable to most text generation methods that are supervised by a large corpus. In this paper, we propose an unsupervised approach to pun generation based on lots of raw (unhumorous) text and a surprisal principle. Specifically, we posit that in a pun sentence, there is a strong association between the pun word (e.g., “dyed”) and the distant context, but a strong association between the alternative word (e.g., “died”) and the immediate context. We instantiate the surprisal principle in two ways: (i) as a measure based on the ratio of probabilities given by a language model, and (ii) a retrieve-and-edit approach based on words suggested by a skip-gram model. Based on human evaluation, our retrieve-and-edit approach generates puns successfully 30% of the time, doubling the success rate of a neural generation baseline.
%R 10.18653/v1/N19-1172
%U https://aclanthology.org/N19-1172
%U https://doi.org/10.18653/v1/N19-1172
%P 1734-1744

Markdown (Informal)

[Pun Generation with Surprise](https://aclanthology.org/N19-1172) (He et al., NAACL 2019)

• Pun Generation with Surprise (He et al., NAACL 2019)

ACL

• He He, Nanyun Peng, and Percy Liang. 2019. Pun Generation with Surprise. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1734–1744, Minneapolis, Minnesota. Association for Computational Linguistics.