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Computational Humor: Can a machine have a sense of humor? (2020)

Can computers have a sense of humor? In this talk, we discuss some dimensions of computational humor, where the research field stands and showcase some of Thomas Winters' work in this field. This talk has been given multiple times by Thomas Winters, in particular on the 11th of December 2020 as a DTAI seminar (KU Leuven's Declarative Languages and Artificial Intelligence Research lab) More information of this talk on https://thomaswinters.be/talk/2020dtai Abstract: Can a machine have a sense of humor? At first glance, this question may seem paradoxical, given that humor is an intrinsically human trait. By limiting the scope to specific types of jokes and by hand-coding rules for them, researchers generally have been able to create several methods for detecting and generating humor. Recently, large scale pre-trained language models like BERT, GPT-2/GPT-3 and our own Dutch RobBERT model opened the way for learning even better insights into humor. In this talk, we provide an overview of the history of computational humor, discuss several types of humor tasks that have been automated using artificial intelligence, illustrate several useful applications of computational humor and position several of our own research projects in this field.

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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?
2
Can a machine have a
sense of humor?
3
Part 1: Humor
Intrinsically human!
4
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)
5
Purpose of Humor = Sign of Intelligence?
6
Purpose of Humor = Sign of Intelligence?
huh?

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Computational Humor: Can a machine have a sense of humor? (2020)

  • 1. 1 Computational Humor Thomas Winters PhD Student at KU Leuven & FWO Fellow @thomas_wint thomaswinters.be Can a machine have a sense of humor?
  • 2. 2 Can a machine have a sense of humor?
  • 4. 4 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)
  • 5. 5 Purpose of Humor = Sign of Intelligence?
  • 6. 6 Purpose of Humor = Sign of Intelligence? huh?
  • 7. 7 Purpose of Humor = Sign of Intelligence? huh? aha!
  • 8. 8 Purpose of Humor = Sign of Intelligence? huh? aha! that’s funny
  • 9. 9 Purpose of Humor = Sign of Intelligence? huh? aha! that’s funny h
  • 10. 10 Purpose of Humor = Sign of Intelligence? huh? aha! that’s funny Brain is rewarding noticing incongruities and successfully resolving them h
  • 11. 11 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
  • 12. 12 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
  • 14. 14 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?”
  • 15. 15 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
  • 16. 16 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
  • 17. 17 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
  • 18. 18 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
  • 19. 19 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!
  • 20. 20 Humor = AI-complete? ~ as hard as making computers as “intelligent” as people
  • 21. 21 Part 2: Humor Generation
  • 23. 23 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
  • 24. 24 JAPE: Templates & Schemas Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles
  • 25. 25 JAPE: Templates & Schemas What’s <CharacteristicNP> and <Characteristic1> ? A <Word1> <Word2>. Binsted, K., & Ritchie, G. (1994). An implemented model of punning riddles
  • 26. 26 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
  • 27. 27 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
  • 28. 28 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
  • 29. 29 Unsupervised Analogy Generator Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data
  • 30. 30 Unsupervised Analogy Generator I like my <X> like I like my <Y>: <Z> Petrović, S., & Matthews, D. (2013). Unsupervised joke generation from big data
  • 31. 31 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
  • 32. 32 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
  • 33. 33 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
  • 34. 34 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
  • 35. 35 Talk Generator Try it yourself: talkgenerator.com Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
  • 36. 36 Talk Generator Try it yourself: talkgenerator.com Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
  • 37. 37 Talk Generator Try it yourself: talkgenerator.com Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
  • 38. 38 Talk Generator Try it yourself: talkgenerator.com Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
  • 39. 39 Talk Generator Try it yourself: talkgenerator.com Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
  • 40. 40 Talk Generator Try it yourself: talkgenerator.com Winters T., Mathewson K. (2019). Automatically Generating Engaging Presentation Slide Decks
  • 41. 41 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
  • 42. 42 E.g. to easily program Samson Twitterbot Winters, T. (2019). Modelling Mutually Interactive Fictional Character Conversational Agents
  • 43. 43 Learning a sense of humor?
  • 44. 44 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
  • 45. 45 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
  • 46. 46 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
  • 47. 47 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"
  • 48. 48 Reminiscent of how children write jokes
  • 49. 49 Let’s try simpler & more understandable models!
  • 50. 50 Dynamic Templates Winters, T. (2019). Generating philosophical statements using interpolated markov models and dynamic templates. Used in @TorfsBot
  • 51. 51 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
  • 52. 52 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
  • 53. 53 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
  • 54. 54 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
  • 55. 55 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
  • 56. 56 GITTA: Template Trees for extracting templates Winters, T., & De Raedt, L. (2020). Discovering Textual Structures: Generative Grammar Induction using Template Trees. Input
  • 57. 57 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
  • 58. 58 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
  • 59. 59 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
  • 60. 60 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.
  • 61. 61 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.
  • 62. 62 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.
  • 63. 63 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.
  • 64. 64 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.
  • 65. 65 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.
  • 66. 66 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.
  • 67. 67 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.
  • 68. 68 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.
  • 70. 70 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.
  • 72. 72 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.
  • 73. 73 GAG: implements GOOFER framework Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples
  • 74. 74 GAG: implements GOOFER framework Winters, T., Nys, V., & De Schreye, D. (2018). Automatic Joke Generation: Learning Humor from Examples
  • 75. 75 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
  • 76. 76 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
  • 77. 77 Need for good humor detection algorithms!
  • 78. 78 Part 3: Humor Detection
  • 79. 79 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.
  • 80. 80 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?
  • 81. 81 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
  • 82. 82 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
  • 83. 83 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
  • 84. 84 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
  • 85. 85 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
  • 86. 86 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
  • 87. 87 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
  • 88. 88 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
  • 92. 92 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

Editor's Notes

  1. Haha and Aha are very similar
  2. Haha and Aha are very similar
  3. Haha and Aha are very similar
  4. Haha and Aha are very similar
  5. Haha and Aha are very similar
  6. Haha and Aha are very similar
  7. Haha and Aha are very similar
  8. Haha and Aha are very similar
  9. Generated 100 jokes to JokeJudger This = 9500 ratings & markings First three categories: all jokes Classifier > Regression In half of the times, the jokes of the computer are equally good as human jokes. CAN BE FILTERED BY HUMANS!
  10. Generated 100 jokes to JokeJudger This = 9500 ratings & markings First three categories: all jokes Classifier > Regression In half of the times, the jokes of the computer are equally good as human jokes. CAN BE FILTERED BY HUMANS!
  11. Generated 100 jokes to JokeJudger This = 9500 ratings & markings First three categories: all jokes Classifier > Regression In half of the times, the jokes of the computer are equally good as human jokes. CAN BE FILTERED BY HUMANS!
  12. Generated 100 jokes to JokeJudger This = 9500 ratings & markings First three categories: all jokes Classifier > Regression In half of the times, the jokes of the computer are equally good as human jokes. CAN BE FILTERED BY HUMANS!
  13. Why need computational humour? Let’s look at Virtual Assistants