The document discusses computational humor and whether machines can have a sense of humor. It explores several attempts at humor generation including hard-coding joke templates, using analogy generators to create jokes from word associations, and generating nonsense presentations. It also discusses limitations of current large language models for humor and proposes simpler models using templates and grammars may be better able to understand and generate humor.

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Computational Humor
Thomas Winters
PhD Student at KU Leuven & FWO Fellow
@thomas_wint
thomaswinters.be
Can a machine have a sense of humor?

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Can a machine have a
sense of humor?

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Part 1: Humor
Intrinsically human!

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Every known
human civilisation
has some form of
humor
Caron, J.E.: From ethology to aesthetics: Evolution as a theoretical paradigm for research on laughter, humor, and other comic phenomena. Humor15(3), 245–281(2002)

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Purpose of Humor = Sign of Intelligence?

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Purpose of Humor = Sign of Intelligence?
huh?

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Purpose of Humor = Sign of Intelligence?
huh?
aha!

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Purpose of Humor = Sign of Intelligence?
huh?
aha!
that’s
funny

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Purpose of Humor = Sign of Intelligence?
huh?
aha!
that’s
funny
h

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Purpose of Humor = Sign of Intelligence?
huh?
aha!
that’s
funny
Brain is rewarding noticing
incongruities and
successfully resolving them
h

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Purpose of Humor = Sign of Intelligence?
huh?
aha!
that’s
funny
Brain is rewarding noticing
incongruities and
successfully resolving them
+ linguistic skills
(required to create jokes)
h

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Purpose of Humor = Sign of Intelligence?
huh?
aha!
that’s
funny
Brain is rewarding noticing
incongruities and
successfully resolving them
+ linguistic skills
(required to create jokes)
= Evolutionary advantage!
h

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How does humor work?

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Incongruity-Resolution Theory
Based on: Ritchie, G. (1999). Developing the incongruity-resolution theory.
Two fish are in a tank.
Says one to the other:
“Do you know how to
drive this thing?”

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Incongruity-Resolution Theory
Based on: Ritchie, G. (1999). Developing the incongruity-resolution theory.
Two fish are in a tank.
Says one to the other:
“Do you know how to
drive this thing?”
Setup

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Incongruity-Resolution Theory
Based on: Ritchie, G. (1999). Developing the incongruity-resolution theory.
Obvious
Interpretation
Two fish are in a tank.
Says one to the other:
“Do you know how to
drive this thing?”
Setup

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Incongruity-Resolution Theory
Based on: Ritchie, G. (1999). Developing the incongruity-resolution theory.
Obvious
Interpretation
Two fish are in a tank.
Says one to the other:
“Do you know how to
drive this thing?”
Setup
Punchline

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Incongruity-Resolution Theory
Based on: Ritchie, G. (1999). Developing the incongruity-resolution theory.
Obvious
Interpretation
Hidden
Interpretation
Two fish are in a tank.
Says one to the other:
“Do you know how to
drive this thing?”
Setup
Punchline

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Human-focused definition!
Machine should not only spot
two mental images
Obvious
Interpretation
Hidden
Interpretation
But also this is
not too hard or too easy for a human!

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Humor = AI-complete?
~ as hard as making computers as “intelligent” as people

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Part 2: Humor Generation

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Hard-coding a sense of humor

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JAPE Joke Examples
What is the difference
between leaves and a car?
One you brush and rake, the
other you rush and brake
What do you call a
strange market?
A bizarre bazaar.
Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

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JAPE: Templates & Schemas
Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

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JAPE: Templates & Schemas
What’s <CharacteristicNP>
and <Characteristic1> ?
A <Word1> <Word2>.
Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

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JAPE: Templates & Schemas
What’s <CharacteristicNP>
and <Characteristic1> ?
A <Word1> <Word2>.
Noun Phrase
Word1 Word2
Homophone1
Characteristic1 CharacteristicNP
Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

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JAPE: Templates & Schemas
What’s <CharacteristicNP>
and <Characteristic1> ?
A <Word1> <Word2>.
Noun Phrase
Word1 Word2
Homophone1
Characteristic1 CharacteristicNP
spring (season)
to bounce
spring (elastic body)
cabbage
green
spring cabbage
Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

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JAPE: Templates & Schemas
What’s <CharacteristicNP>
and <Characteristic1> ?
A <Word1> <Word2>.
Noun Phrase
Word1 Word2
Homophone1
Characteristic1 CharacteristicNP
What’s green and
bounces?
A spring cabbage.
spring (season)
to bounce
spring (elastic body)
cabbage
green
spring cabbage
Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles

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Unsupervised Analogy Generator
Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data

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Unsupervised Analogy Generator
I like my <X> like
I like my <Y>:
<Z>
Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data

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Unsupervised Analogy Generator
I like my <X> like
I like my <Y>:
<Z>
Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data
Z
Y
X
Maximise adjective
dissimilarity
Maximise co-occurrence Maximise co-occurrence
Maximise # definitions
Minimize commonness

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Unsupervised Analogy Generator
I like my <X> like
I like my <Y>:
<Z>
Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data
cold
war
coffee
Z
Y
X
Maximise adjective
dissimilarity
Maximise co-occurrence Maximise co-occurrence
Maximise # definitions
Minimize commonness

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Unsupervised Analogy Generator
I like my <X> like
I like my <Y>:
<Z>
I like my coffee
like I like my war:
cold.
Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data
cold
war
coffee
Z
Y
X
Maximise adjective
dissimilarity
Maximise co-occurrence Maximise co-occurrence
Maximise # definitions
Minimize commonness

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Talk Generator
Generates nonsense PowerPoints about any given topic
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
Try it yourself: talkgenerator.com

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Talk Generator
Try it yourself: talkgenerator.com
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

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Talk Generator
Try it yourself: talkgenerator.com
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

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Talk Generator
Try it yourself: talkgenerator.com
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

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Talk Generator
Try it yourself: talkgenerator.com
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

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Talk Generator
Try it yourself: talkgenerator.com
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

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Talk Generator
Try it yourself: talkgenerator.com
Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks

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Templates + Grammar + Functions = Babbly
Created programming language for templates text
generation with grammars for more variation: Babbly
import firstname.words
food = pasta|pizza|sushi
main = {
3: <firstname> loves <food.uppercase>!
1: <firstname> (quite|reasonably|fairly) likes <food>. Oo{1,3}h, I hope they join!
1: <firstname:protagonist> is not (quite){.5} fond of <food:>.
<firstname:protagonist> will thus not go to the <food:> (restaurant|place).
}
Winters, T. (2019). Modelling Mutually Interactive Fictional Character Conversational Agents

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E.g. to easily program Samson Twitterbot
Winters, T. (2019). Modelling Mutually Interactive Fictional Character Conversational Agents

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Learning a sense of humor?

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Transformer models
Large language models, pretrained on large corpora
Outperforming previous neural architectures
on most language tasks
GPT-2 & GPT-3
Completes any textual prompt
BERT
Classifies any text sequence / token
Brown, Tom B., et al. "Language models are few-shot learners."
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding

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Not just for English!
Phase 1: Pretraining:
• Find unlabeled corpus for target language
• Expensive pretraining phase
• BUT: reusable! Just download existing!
Phase 2: Finetuning a pre-trained BERT
• Feed labeled training data
• Relatively cheap fine-tuning for the target task
• Usually outperforms other language models on
most NLP tasks
Delobelle, P., Winters, T., & Berendt, B. (2020). RobBERT: a dutch RoBERTa-based language model.
RobBERT
Our Dutch BERT-like model

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GPT-2 / GPT-3 humor?
Tokenizer problem: maps words to numbers
 Remove knowledge about letters!
https://www.gwern.net/GPT-3
https://towardsdatascience.com/teaching-gpt-2-a-sense-of-humor-fine-tuning-large-transformer-models-on-a-single-gpu-in-pytorch-59e8cec40912
Hard to make/recognize new wordplay!
“tank” 28451
“thank” 40716

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GPT-2 / GPT-3 joke examples
GPT-2 can mimic joke style, but usually very absurd
https://www.gwern.net/GPT-3
https://towardsdatascience.com/teaching-gpt-2-a-sense-of-humor-fine-tuning-large-transformer-models-on-a-single-gpu-in-pytorch-59e8cec40912
What did one plate say
to the other plate?
Dip me!
What did the chicken
say after he got hit by a
bus?
"I'm gonna be fine!"
A woman walks into the bar......she
says to the bartender "I want a
double entendre" and the
bartender says "No, we don't
serve that"

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Reminiscent of how children write jokes

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Let’s try simpler & more
understandable models!

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Dynamic Templates
Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates.
Used in @TorfsBot

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Dynamic Templates
“Are there also atheists that don't believe in atheism?”
“They see the fact that the former God is not trying
to deny this newly acquired insight as proof of
them being right. Even with the Church, things are
not going well. Norse popes.”
Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates.
Used in @TorfsBot

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Dynamic Templates
“Are there also atheists that don't believe in atheism?”
“They see the fact that the former God is not trying
to deny this newly acquired insight as proof of
them being right. Even with the Church, things are
not going well. Norse popes.”
Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates.
Used in @TorfsBot

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Dynamic Templates
“Are there also atheists that don't believe in atheism?”
“They see the fact that the former God is not trying
to deny this newly acquired insight as proof of
them being right. Even with the Church, things are
not going well. Norse popes.”
Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates.
Used in @TorfsBot
Replace some matching
words with matching POS
tag with lowest frequencies

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Dynamic Templates
“Are there also atheists that don't believe in atheism?”
“They see the fact that the former God is not trying
to deny this newly acquired insight as proof of
them being right. Even with the Church, things are
not going well. Norse popes.”
Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates.
Used in @TorfsBot
Replace some matching
words with matching POS
tag with lowest frequencies

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Dynamic Templates
“Are there also atheists that don't believe in atheism?”
“They see the fact that the former God is not trying
to deny this newly acquired insight as proof of
them being right. Even with the Church, things are
not going well. Norse popes.”
“Are there also popes that don't
believe in God?”
Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates.
Used in @TorfsBot
Replace some matching
words with matching POS
tag with lowest frequencies

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GITTA: Template Trees for extracting templates
Winters, T., & De Raedt, L. (2020). Discovering Textual Structures: Generative Grammar Induction using Template Trees.
Input

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GITTA: Template Trees for extracting templates
1. Join closest strings
2. Merge similar template slots
3. Iteratively simplify until convergence
Winters, T., & De Raedt, L. (2020). Discovering Textual Structures: Generative Grammar Induction using Template Trees.
Input

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GITTA: Template Trees for extracting templates
1. Join closest strings
2. Merge similar template slots
3. Iteratively simplify until convergence
Winters, T., & De Raedt, L. (2020). Discovering Textual Structures: Generative Grammar Induction using Template Trees.
Input

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GITTA: Template Trees for extracting templates
1. Join closest strings
2. Merge similar template slots
3. Iteratively simplify until convergence
A: I like my <B> and my <B>
| <G> the <B> is <F>
B: chicken | cat | dog
F: walking | jumping
G: Alice | Bob | Cathy
Winters, T., & De Raedt, L. (2020). Discovering Textual Structures: Generative Grammar Induction using Template Trees.
Input

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Generalising Schemas to Enable Machine Learning
Constraint-based Schemas
Hard to tweak to preference
Metrical Schema
Allows for learning in
aggregator component
Header
Keywords
Template
Features
Aggregator
Values Generator
Humour
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
1.
Training
a) I like my relations like I like my source, open.
b) I like my coffee like I like my war, cold.
c) The sailor bears a stress. Pier pressure.
d) I like my girlfriend like I like my estate: real.
e) The lord cultivates a region. A baron land.
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
1.
Training
Joke Rater 1 Rater 2 Rater 3
a 5 4 5
b 4 4 3
c 5 3 4
d 2 1 3
e 3 2 2
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
1.
Training
Templates
Template 1: “I like my X like I like my Y, Z.”
relations, source, open | [5,4,5]
coffee, war, cold | [4,4,3]
girlfriend, estate, real | [2,1,3]
Template 2: ``The A B a C. D.‘’
sailor, bears, stress, Pier pressure | [5,3,4]
lord, cultivates, region, A baron land | [3,2,2]
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
1.
Training
Templates
X Y Z Score FreqX FreqY FreqZ FreqXY …
relations source open [5,4,5] 3 831 210 4 050 904 4884757 0 …
coffee war cold [4,4,3] 784 065 9 211 826 2 010 173 0 …
girlfriend estate real [2,1,3] 75 392 998 669 5 284 057 0 ….
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
1.
Training
Templates
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
Input
topic
1.
Training
2.
Generating
Templates
“Men”
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
Input
topic
Word +
Template
1.
Training
2.
Generating
Templates
“Men” + “I like my X
like I like my Y, Z.”
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

69. 69
Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
Input
topic
Word +
Template
Generated
template values
1.
Training
2.
Generating
Templates
a) men, grave, nameless
b) men, country, cold
c) men, turkey, roast
d) men, rain, gentle
e) men, laugh, silly
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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X Y Z Score FreqX FreqY FreqZ FreqXY …
men grave nameless ? 9 876 543 4 050 904 4884757 0 …
men country cold ? 9 876 543 9 211 826 2 010 173 0 …
men turkey roast ? 9 876 543 998 669 5 284 057 0 ….
men rain gentle ? 9 876 543 9 876 543 845 321 0 …
men laugh silly ? 9 876 543 954 569 123 456 0 ….
Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
Input
topic
Word +
Template
Generated
template values
Template values
+ features
1.
Training
2.
Generating
Templates
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

71. 71
Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
Input
topic
Word +
Template
Generated
template values
Template values
+ features
1.
Training
2.
Generating
Templates
Filtered
template
values set
men, grave, nameless => 5
men, country, cold => 1
men, turkey, roast =>3
men, rain, gentle =>4
men, laugh, silly => 1
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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Human
Evaluation
Template
Extractor
Template
Store
Values
Generator
Metric-based
Rater
Classifier/
Regression
Input
Jokes
Rated
Jokes
Set of rated template
values per template
Rated template values with
feature values (training
data)
Input
topic
Word +
Template
Generated
template values
Template values
+ features
Jokes
about
seed
1.
Training
2.
Generating
(Apply
Template)
Templates
Filtered
template
values set
I like my men like I like my grave: nameless
I like my men like I like my rain: gentle
GOOFER: Generalised framework
Winters, T., Nys, V., & De Schreye, D. (2019). Towards a general framework for humor generation from rated examples.

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GAG: implements GOOFER framework
Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples

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GAG: implements GOOFER framework
Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples

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GAG: implements GOOFER framework
I like my coffee like I like my sleep: strong.
I like my men like I like my graves: nameless.
I like my women like I like my tests: independent.
Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples

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GAG: implements GOOFER framework
11.41%
22.61%
21.08%
GAG: Classifier Human-created
on JokeJudger
Human-created
single-word
jokes
4+ RATINGS FREQUENCY
Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples

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Need for good
humor detection algorithms!

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Part 3: Humor Detection

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Early Humor Detector
• Designed humor features e.g. alliteration, antonym, adult slang...
• Used Naive Bayes and Support Vector Machines
• Task: One-liners vs news, neutral corpus & proverbs
Mihalcea, R., & Strapparava, C. (2005). Making computers laugh: Investigations in automatic humor recognition.

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But is this a good dataset?
News & proverbs have completely different types
of words than jokes!
 Looking at word frequencies is often already “enough”!
Is this really humor detection?

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Jokes are fragile!
Two fish are in a tank. Says one to the other:
“Do you know how to drive this thing?”
Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

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Jokes are fragile!
Two fish are in a tank. Says one to the other:
“Do you know how to drive this thing?”
Generate non-jokes using dynamic templates!
Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

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Jokes are fragile!
Two fish are in a tank. Says one to the other:
“Do you know how to drive this thing?”
men
Generate non-jokes using dynamic templates!
Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

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Jokes are fragile!
Two fish are in a tank. Says one to the other:
“Do you know how to drive this thing?”
men bar
Generate non-jokes using dynamic templates!
Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

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Jokes are fragile!
Two fish are in a tank. Says one to the other:
“Do you know how to drive this thing?”
men bar
Generate non-jokes using dynamic templates!
Word-based features won’t work anymore!
Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

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51%
60%
50%
94% 94%
47%
94% 94%
47%
99% 96%
89%
Jokes vs News Jokes vs Proverbs Jokes vs Generated Jokes
Binary classification of jokes versus texts from other domains
Naive Bayes LSTM CNN RobBERT
Much more challenging dataset!
More truthful humor detection?
Winters T., Delobelle P. (2020). Dutch humor detection by generating negative examples. BNAIC/Benelearn2020

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GALMET: Genetic Algorithm using Language
Models for Evolving Text
Created algorithm that evolves headlines into satirical headlines
Winters T., Delobelle P. (UNPUBLISHED). Survival of the Wittiest: Evolving Satire with Language Models

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Survival of the Wittiest
Amazon removes Indian flag doormat after minister threatens visa ban
 NASA releases rainbow leg doormat after violating OT ban
Winters T., Delobelle P. (UNPUBLISHED). Survival of the Wittiest: Evolving Satire with Language Models

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Symbiotic relationship
Can help
improve
Humor
Generation
Can help
improve
Humor
Detection

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Part 4: Why?

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Grammarly but for jokes?

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VIRTUAL ASSISTANTS
People often treat virtual
assistants like humans
(“please”, “thank you”, “sorry”) [1]
[1] https://www.thinkwithgoogle.com/consumer-insights/voice-assistance-consumer-experience/
[2] Laughter’s Influence on the Intimacy of Self-Disclosure, Gray, A.W., Parkinson, B. & Dunbar, R.I. Hum Nat (2015) 26: 28.
41%
say it’s like talking
to a friend [1]
Humor and creativity in
language is important
between friends [2]

93. 93
Casual Creation: it’s fun to create these little bots!
https://thomaswinters.be/mopjesbot Winters, T. (2019). Generating Dutch Punning Riddles about Current Affairs

94. 94
Mutually interactive bots
Winters, T. (2019). Modelling Mutually Interactive Fictional Character Conversational Agents
https://thomaswinters.be/samsonbots

95. 95
Studying & understanding humor
We do not fully know how humor works
Artificial Intelligence can shed a light!
Humour

96. 96
Can computers have a sense of humor?
Learn rules if
enough data
Hand-craft rules
for type of humor
Better NLP models
get us closer & closer
Humour

97. 97
BUT:
AI-complete problem!
Need to process two mental images
before fully understanding the joke

98. 98
Computational humor is still in its infancy
But, children have a fascinating sense of humor

99. 99
Thank you!
Some images (based on the works) of dooder on freepik.com
Thomas Winters
PhD Student at KU Leuven & FWO Fellow
@thomas_wint
thomaswinters.be