=Paper=
{{Paper
|id=Vol-3180/paper-139
|storemode=property
|title=Humorous Wordplay Generation in French
|pdfUrl=https://ceur-ws.org/Vol-3180/paper-139.pdf
|volume=Vol-3180
|authors=Loic Glemarec,Anne-Gwenn Bosser,Julien Boccou,Liana Ermakova
|dblpUrl=https://dblp.org/rec/conf/clef/GlemarecBBE22
}}
==Humorous Wordplay Generation in French==
https://ceur-ws.org/Vol-3180/paper-139.pdf
Humorous Wordplay Generation in French
Loic Glémarec1 , Anne-Gwenn Bosser2 , Julien Boccou1 and Liana Ermakova3,4
1
Université de Bretagne Occidentale, Brest, France
2
ENIB, Brest, Lab-STIC CNRS UMR6285
3
Université de Bretagne Occidentale, HCTI, Brest, France
4
Maison des sciences de l’homme en Bretagne, Rennes, France
Abstract
Recent work have tackled the problem of generating puns in English, based on the corpus of English
puns from SemEval 2017 Task 7. In this paper, we report on experiments on generating French puns
based on the data released for the CLEF 2022 JOKER and inspired by methods for generating English
puns with large pretrained models. 50% of generated wellerisms were funny.
Keywords
Computational Humour, Humour generation, Wordplay, Wellerism, Word embedding, Lexique 3, Large
pre-trained models, Few-shot learning, Computational creativity
1. Introduction
Humour aims to provoke laughter and provide amusement. The appropriate use of humour
can facilitate social interactions [1] as it can reduce awkward, uncomfortable, or uneasy feelings.
Humour contributes to higher physical and psychological wellbeing and has shown to be
effective to cope with distress [2]. Indeed, according to the benign-violation theory, ’humour
only occurs when something seems wrong, unsettling, or threatening, but simultaneously seems
okay, acceptable or safe’ [3]. Wordplay is a common source of humor because of its subversive
and catchy nature. Recent work by [4] have tackled the issue for generating humourous puns in
English based on the data provided by [5]. The CLEF Joker Workshop [6, 7] provided a similar
dataset for the French language, and allowed us to investigate how well this method could
be transposed in French. In the work by [4], the goal is to generate puns in English, relying
on paronyms and a modification of the context of the sentence to create surprise, resolving
the incongruity that will result in the humourous effect, by applying the pun at the end of
sentences. Despite the generality of this principle, most of the published work that we could
find on computational humor generation remains primarily for the English language. The work
described in [8] makes use of constraints to provide structurally correct and successfully funny
wordplay. Their approach, which rely less on statistical linguistic resources than the most recent
CLEF 2022: Conference and Labs of the Evaluation Forum, September 5–8, 2022, Bologna, Italy
$ Loic.Glemarec1@etudiant.univ-brest.fr (L. Glémarec); bosser@enib.fr (A. Bosser);
julien.boccou@etudiant.univ-brest.fr (J. Boccou); liana.ermakova@univ-brest.fr (L. Ermakova)
https://yamadharma.github.io/ (L. Glémarec); https://www.joker-project.com/ (L. Ermakova)
0000-0002-7598-7474 (L. Ermakova)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
literature, seems appropriate for languages other than English for which these might currently
be less performing. It was also an inspiration for our work.
In this paper we show how to generate puns for the French language and that the method
works for English as well. We describe a three-steps method: first, we select the word on which
to apply wordplay; second, we select one of the most distant homophone semantically, and
finally, we look for a novel context consistent with the homophone operated by prediction
using a large language model. Using this method, we were able to generate grammatically but
also structurally correct wordplay sentences (eg. following the expected pattern or template).
Although we have not yet completed a full evaluation of the output, we curated a number of
potentially humorous results some of which are provided in this paper.
The incongruity of the expected and given stimuli is also used in wellerisms which exploits
the contradiction of figurative and literal meanings. Wellerisms are wordplays that make fun
of established clichés and proverbs in a context where they are taken literally [9]. Thus, we
also explore the effectiveness of large pre-trained models, such as GPT-3 [10], for wellerism
generation.
2. Method
2.1. 5-step wordplay generation
Our goal is to generate wordplay based on homophones from a simple sentence without any
sense of humor. Wordplay generation is built in 5 distinct steps. To do this, we must first
locate the word on which to apply the wordplay: 𝑤𝑝𝑖𝑐𝑘 (PICK). Then, when we have selected a
word, we search the list of all its homophones and select one: 𝑤𝑠𝑤𝑎𝑝 (SWAP). There is a subject
detection step: 𝑤𝑠𝑢𝑏𝑗𝑒𝑐𝑡 (SUBJECT). To accentuate the humorous effect we need to change the
topic to correspond to 𝑤𝑠𝑤𝑎𝑝 so that the context is consistent: 𝑤𝑡𝑜𝑝𝑖𝑐 (TOPIC). Finally, once all
these generation elements have been brought together, it is possible to rebuild the sentence in
the pun format (REBUILD).
PICK For the word selection, we started by listing all the adjectives and nouns of the sentence.
For this we use the lexicon Lexique 3 for the French language [11, 12]. We proceeded iteratively
and looked for homophones with the same part of speech (adjectives, nouns,...). To ensure
grammatical correctness, we limited the selection to words that do not have two possible part
of speech for the same spelling. When this is the case, the word is qualified as ambiguous and
will therefore not be included in the list of possible targets for wordplay. We check the number
of homophones that is information given by Lexique 3. If a noun or adjective does not have at
least one homophone, it is also removed from the list. Finally, when several words are found to
have homophones in the sentence, we select the word closest to the end. This choice is made in
order to maximise the surprise and thus the potential comic effect, following the argument in
the original paper by [4].
SWAP Before proceeding with the exchange, we first listed all the homophones by comparing
their phonetic form which is provided as part of Lexique 3. We also added constraints to improve
the selection with other key information:
• Flag 1 : The lemmatized form must not be the same as the initial word 𝑤𝑝𝑖𝑐𝑘 .
• Flag 2 : The homophone part of speech must be the same as that of the initial word 𝑤𝑝𝑖𝑐𝑘 .
This allows to keep the grammatical coherence.
• Flag 3 : The frequency of occurrence of the homophone must be greater than 2, this infor-
mation is also given using Lexique 3. Indeed, the two fields freqlemfilms and freqlemlivres
respectively represent the frequency of the lemma according to a corpus of subtitles and
the frequency of the lemma according to a corpus of books (both are given per million
occurrences). This avoids replacing with a word too little known and therefore creates a
feeling of incomprehension.
When these three conditions are met, but there are still several possible homophones, the
𝑤𝑠𝑤𝑎𝑝 will be the homophone that is the most semantically distant from 𝑤𝑝𝑖𝑐𝑘 . The choice of
a semantically distant word permits the selection of the one that will have the most distant
context possible. By doing this, the comic effect will be accentuated by an increased surprise.
To compare the semantic distance of words, we use the French version of fasttext [13]1 . This
model allows mapping a word to a vector value. The comparison is done by measuring the
distance between two word-vectors. The greater the distance, the more semantically distant the
two words are. To calculate the distance, we compute the cosine value of the angle formed by
the two vectors.
When these operation are successfully completed, we end up with one homophone (𝑤𝑠𝑤𝑎𝑝 )
that will provide a grammatically correct substitution and will maximise the humourous poten-
tial. The next step is to provide a topic change in the sentence.
SUBJECT Before topic change in the sentence, we need to detect the subject (𝑤𝑠𝑢𝑏𝑗𝑒𝑐𝑡 ). It is
is achieved through Jurassic2 [14], a Large Language Model. To do so, we provide the model
with several examples (see in A) of sentences while highlighting their subject. This information
will be use in next step and will permit a better generation precision.
TOPIC The sentence topic change is also operated through Jurassic. As in the research we
based our proposal on, the topic change is made by changing a word in the sentence. As in
previous step, we provide the model with several examples (B), which were constructed using
the dataset from the CLEF 2022 Joker Workshop [6] then we request the prediction of a new
topic for the setup provided by the previous steps for some other example from the test dataset.
Intending to guide the prediction toward what we are interested in, i.e. consistency between
the new homophone and the topic, we give as information:
• The initial sentence
• The 𝑤𝑝𝑖𝑐𝑘 word
• The 𝑤𝑠𝑤𝑎𝑝 homophone
• The 𝑤𝑠𝑢𝑏𝑗𝑒𝑐𝑡 to change
We asked for the generation of 15 predictions. This is followed by the removal of duplicates
before selecting the subject (𝑤𝑡𝑜𝑝𝑖𝑐 ) most semantically close to 𝑤𝑠𝑤𝑎𝑝 .
1
https://fasttext.cc/
2
https://studio.ai21.com/docs/jurassic1-language-models/
REBUILD Finally, it is now possible to reconstruct the pun. Again thanks to Jurassic and
providing the following information (C):
• The initial sentence
• The 𝑤𝑠𝑢𝑏𝑗𝑒𝑐𝑡 word
• The 𝑤𝑡𝑜𝑝𝑖𝑐 word
The pun is therefore similar with respect to the initial sentence, but the subject 𝑤𝑠𝑢𝑏𝑗𝑒𝑐𝑡 has
been changed to 𝑤𝑡𝑜𝑝𝑖𝑐 to ensure contextual consistency with the homophone 𝑤𝑠𝑤𝑎𝑝 of the
word 𝑤𝑝𝑖𝑐𝑘 .
2.2. Wellerism generation with large pre-trained models
Wellerisms are wordplay that make use of catchphrases, phrases or expressions recognized
by their repeated utterance. Wellerisms are a common type of wordplay with recognizable
conventional form which helps to prepare a joke. We generated the following types of wellerisms
that were:
• Question-Answer. This type of wellerisms refers to bipartite jokes with the form of a
question followed by an answer.
Example 2.1. Qu’est-ce que l’étudiant dit à la calculatrice? Tu comptes beaucoup pour
moi.
• Old soldiers never die wellerisms are transformations of the catchphrase, with the full
version being Old soldiers never die, they simply fade away.
Example 2.2. Les vieux électriciens ne meurent pas, ils 100 volts.
• Tom swifty are wellerisms with a phrase in which a quoted sentence is linked by a pun
to the manner in which it is attributed. The standard form is for the quoted sentence to be
first, followed by the description of the act of speaking of the conventional speaker Tom
Example 2.3. "J’ai commencé à lire Voltaire", avoua Tom d’un ton candide.
To generate these types of wellerisms, we used prompt-tuning of large-pretrained models,
namely GPT-3 [10]. Discrete prompt-tuning is a widely-used technique to condition frozen
language models to perform specific downstream tasks [15]. We considered the generation of
each type of these wellerisms as an individual task.
The prompts were generated automatically based on the data in French constructed at the
JOKER workshop [6, 7]. We applied regular expressions to extract Question-Answer, Old soldiers
never die and Tom swifty wellerisms from the corpus. We generated a training prompt for each
category by randomly selecting small training set from the corresponding subcorpus, i.e. we
used three distinct training prompts in total. The same training prompt was applied for all
generations. As all these wellerisms are bipartite, we split wordplay into to parts and we used
the first part of each wordplay for the generation.
Table 1
Statistics of data used for 5-step generation
Step # requests number # train instances # output
TOPIC 1 5 15
SUBJECT 1 6 1
REBUILD 1 5 1
Table 2
Statistics of data used for wellerism generation
Wellerism category # train instances # test instances # total in corpus
Question-Answer 20 50 392
Old soldiers never die 10 40 272
Tom swifty 20 50 503
3. Evaluation framework
3.1. Data description
Our data is twofold, containing human and machine translations of the SemEval-2017 corpus of
English puns [5] into French [7].
The 5-step wordplay generation aims to transform a non-humorous text into wordplay. Thus,
the source corpus should without wordplay but with a potential to do it. With this consideration
in mind, we used machine translations generated by the participants of JOKER Task 3: Pun
translation from English into French [7]. We used only the machine translations that were
annotated not to contain wordplay. The initial non-wordplay corpus consisted of 6 780 texts.
As machine translations of the same source text might be quite similar, we dropped entries with
duplicated identifiers of source puns in English and, thus, kept only one machine translation per
English pun. Then, we filtered out texts for which we could not find homophones in Lexique-3.
The French wordplay subcorpus used for wellerism generation is a subset of human transla-
tions of the SemEval-2017 English puns [5] produced during at the JOKER translation contest [7].
We used a small subset for the training part of the prompt and 40-50 examples for generation.
Although it is impossible to use a direct comparison of test data with generation for the evalua-
tion due to multiple ways to play on words, the use of joke parts guarantee the possibility of
wordplay. The details of the data statistics is given in Table 2. Although, the corpus contains 272
Old soldiers never die wellerisms, we found only 50 distinct subject. Thus, we used 10 subjects
for training and 40 subjects for test wellerism generation.
3.2. Annotation and evaluation metrics
A master student in translation, French native speaker, manually annotated the produced
generation according to the following binary categories:
• wordplay presence;
Table 3
Results of generation
Category Wordplay Non-sens Truncated Syntax Lexical
problem problem
Question-Answer 8 (15%) 9 2 2 5
Tom swifty 15 (30%) 0 0 11 8
Old soldiers never die 26 (65%) 6 0 1 3
5-step 7(8%) 49 0 2 9
• non-sens;
• truncated text;
• syntax problem;
• lexical problem.
We applied the Likert scale [16] to evaluate joke hilariousness. We applied the scale from 0 to
5 referring to humorless and the funniest texts respectively. The annotator was also asked to
provide free comment on jokes.
We report absolute values as well as the percentage of wordplay in generated texts.
4. Results
Table 3 shows the results of generation. The generation for the category Old soldiers never die
was the most successful, with 65% wordplay produced. Notice that this category is the most
homogeneous, as the beginning varies only in the subject. We observe a significant drop (twice
lower wordplay rate) for the Tom swifty jokes which has a more heterogeneous form and the
lowest results were demonstrated for the Question-Answer type as their form is the less strict.
Figure 1 presents the histogram of the hilariousness scores of generated wellerisms. Almost
50% of generated wellerisms were judged funny by the French native speaker annotator, i.e.
they were attributed a hilariousness score >= 1. The most successful jokes were Tom swifty
while the vast majority of Question-Answering were judged non-humorous. Question-Answering
was the most heterogeneous category.
The statistics on free category for generated wellerisms is given in Table 4. As it is evident
from the table, the Old soldiers never die wellerisms are considered to be euphemisms in 90% of
cases.
Deadpans occur in Question-Answer jokes. Deadpan, also called dry or dry-wit humor, is a
form of comedic delivery with the deliberate display of emotional neutrality contrasting with
the ridiculousness or absurdity of the subject matter [17]. The delivery is meant to be blunt,
ironic, or unintentional. We do not observe deadpans in Tom swifty nor Old soldiers never die
wellerisms as they do not have interaction with interlocutor nor environment.
The results generated using the 5-stepts method are grammatically correct and structurally
correspond to our expectations: the PICK phase keeps the part of speech of the word, and
35 Old
Tom
30 QA
25
Frequency
20
15
10
5
0
0 1 2 3 4 5
Figure 1: Histogram of the hilariousness scores of generated wellerisms
70
60
50
Frequency
40
30
20
10
0
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00
Figure 2: Histogram of the hilariousness scores of 5-step generation
discards ambiguous words on this subject. This limits the number of grammatically false results,
which could harm the humorous effect. The homophonic criteria being easy to use with a
phonetic lexicon and providing obvious wordplay. These may limit the potential generation
when compared to computing paronyms.
In D, you can find examples of puns we created. Each box contains an original sentence, as
well as the built pun. These examples are also used for prompts for jurassic. Then, in E, you
can find the test sentences that fulfill constraints. Sentences in F are all tagged as ambiguous,
Table 4
Free category statistics for generated wellerisms
Category Blunt Absurdism Jokes Euphemism Dark Deadpan One Poetic
for Kids humor liner
QA . 3 3 . 2 5 1 .
Tom 2 4 . . . . . .
Old 2 . . 36 . . 2 3
5-step . 1 . . . . . .
even if they seem to be structurally encouraging, each one contains words that have known
homophones adequately placed in the sentences.
Here are selected results:
• Comte / Compte (Count/Account)
Original sentence : Nous avons beaucoup voyagé, mais grace au comte.
(We have traveled a lot, but thanks to the count.)
Generated: La monnaie a beaucoup voyagé, mais grace au compte.
(The currency has traveled a lot, but thanks to the account.)
• Encre / Ancre (Anchor/Ink)
Original sentence : Le marin au milieu de rien, a jeté son ancre.
(The sailor in the middle of nothing, dropped his anchor.)
Generated : Le poète au milieu de rien, a jeté son encre.
(The poet in the middle of nothing, threw his ink.)
• Mère / Mer (Mother/Sea)
Original sentence : Le fils a dit au revoir à sa mère.
(The son said goodbye to his mother.)
Generated : Le morse a dit au revoir à sa mer.
(The walrus said goodbye to its sea.)
• Seau / Sot (Bucket/Fool)
Original sentence : L’ouvrier a fait tomber un seau.
(The worker dropped a bucket.)
Generated : Un casse-tête a fait tomber un sot.
(A puzzle knocked down a fool.)
The current implementation is a proof of concept, and the generativity of the solution could
be improved in several ways.
The PICK phase provides coherence, but restricted to certain part of speech and forms of
word for simplicity. We will later improve the range of homophones that can be used. We
also plan to provide more variety during the SWAP phase, by expanding the puns to include
paronyms instead of the restricted case of homophones.
This led us to wonder about what criteria to use for deciding when and how paronyms are
perceived and understood by humans. Whilst the answer to this question is likely to be context
specific, skill specific (such as in Contrepèterie/spoonerism identification) and depend on the
media used to communicate the pun (script, voice), we can already consider several criteria to
take into account in identifying which words may be likened by humans in understanding puns,
for instance: phonetic transcription closeness (Hamming distance), similar number of syllables,
similar structure in terms of vowels and consonants, rhymes.
We plan to investigate whether and to which extent various types of punning criteria allow
us to generate more varied and less obvious puns, which may render them more satisfying
depending on the human audiences. Finally, the TOPIC step of our method was merely a first
investigation of Large Language Models and can certainly be improved, especially in terms of
contextual relevance between the homophone and the new topic. We plan to look for more
effective prompts, to influence Jurassic’s prediction.
5. Conclusion
We have presented the results of first investigations for generating humorous puns in French
using large pre-trained language models. The first method is based on work previously done
for the English language, with some adaptation to account for linguistic resources available
for the French language. Our method proceeds in five steps which will transform a sentence:
the PICK step quickly selects the target word for the pun, the SWAP step defines whether the
word is replaceable by a homophone, the SUBJECT step retrieves the input sentence subject,
the TOPIC step which, thanks to the Jurassic model, predicts a new, contextually coherent topic
for the original sentence, and the REBUILD step build the final pun with Jurassic too. We have
presented a few encouraging results. We plan to investigate this topic further: in particular
we would like to work on extending puns to include a variety of structures and heuristics that
humans use to recognize paronyms in punning, and try out different models. We also tried out
the generation of wellerism using the GPT-3 Model, with prompt-tuning using puns sharing
similar templates, with promising results. 50% of generated wellerisms were funny.
6. Online Resources
The sources for the generation of humorous puns in French are available via
• GitLab : https://gitlab.com/loicgle/computational-humor-pun-generation,
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Appendices
Appendix A SUBJECT Jurassic Prompt
Appendix B TOPIC Jurassic Prompt
Appendix C REBUILD Jurassic Prompt
Appendix D Examples of before and after generation
Appendix E French Test Sentences
Appendix F Ambiguous test sentences