* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project
Disclaimer : The jokes during the seminar were generated either by AI (Artificial Intelligence) or by AI (Aaditya’s Intelligence). The bottomline, AI is good. Humour & AI Aditya M Joshi Devshree D Sane 08305908 08305059 [email protected] [email protected] Under the guidance of Dr. Pushpak Bhattacharyya Motivation Why Humour? • Trust •• Interpersonal Attraction Use Existing Intelligent Systems - humans Why Humour & AI? • Stress Release • Computers As Social Actors • Model Intelligent systems • Cognitive science studies as close as possible to them Why AI? “Humour is a powerful weapon - you can even break ice with it.” Scope of the Seminar Humour Recognition Humour Generation Humour & AI Humour theory Computational Humour Humour Recognition JAPE-1 Applications of Computational humour HAHAcronym What is humour? Components • Wit Humour Research • • Challenges • • • • • Mirth Humour theory Sociological Research Laughter Gelotology (Health effects) Different to different people Computational humour Manner Different at different times Theories of humour Superiority theory Relief theory Incongruity theory Dry humour is a form of humour which is narrated as if it is not a joke at all (i.e. narrated in a serious tone, perhaps.) •Focus on feelings necessary •forGives aonnecessary condition Focus effect of humour humour. For humourof – nervous a ‘twist’. energy • Release •• Humour arises fromand showing Mixture of pleasure pain something from at the baseabsurd of amusement something that is not. • Based on contradiction of some sort. Examples of jokes Incongruity theory: "Some people can tell what time it is by looking at the sun. But I have never been able to make out the numbers." Superiority theory: All the “blonde” or “Sardarji” jokes that are cracked. Relief theory: The “battle-of-the-sexes” jokes A pun in Hindi: Sawaal: Shahrukh Khan ne ek sansthaa ko Rs.10000 ka chandaa diya. Us chande ko kya kehte hain? Jawab: “KHAN-DAAN”. Humour & AI Humour theory Computational Humour Humour Recognition JAPE-1 Applications of Computational humour HAHAcronym What is *computational* humour? Definition Areas Our Focus • Using computers in humour research. •• Modelling Humour Generation humour in a computationally tractable way. • Out of all forms, • Humour Recognition text-based / Verbal Humour • Humour in one-liners Computational Humour – Linguistic Ambiguity A word is ambiguous if it has more than one meaning. (‘Ambiguous’ is thankfully not ambiguous. ) Phonological Morphological Syntactic • Same sounds, different meaning. • Three ways: •Words with same surface •Syllable substitution structure. E.g. What do short-sighted ghosts wear? • AsSpooktacles. a result of structure or E.g. : The book is read / red. syntax ofsubstitution sentence. •Word E.g. How do“Squad u make gold soup? • Example: helps dog 14 carrots in it. bitePut victims.” •Metathesis (Reversal of sounds) Humour & AI Humour theory Computational Humour Humour Recognition JAPE-1 Applications of Computational humour HAHAcronym JAPE-1 • Generates question-answer style puns using phonological similarities •For example, What do you give an elephant that’s exhausted? Trunkquillizers. JAPE-1 : Units Lexicon •A set of lexemes. Schemata •Lexeme is an abstract entity, roughly corresponding to a meaning a phrase. which A set ofor relationships Template must hold between lexemes •In addition, aproduce homonym base. To thethe surface form of a joke from the lexemes and relationships specified in an instantiated schema. JAPE-1: Example Lexicon Schemata Template Lexeme : jumper Synonym : Sweater Category: Noun Countable: “What will Yes you get if you cross Specifying adjective : Warm ____ and ____?” Answer: _______ Humour & AI Humour theory Computational Humour Humour Recognition JAPE-1 Applications of Computational humour HAHAcronym HAHAcronym About HAHAcronym Features Examples http://www.haha.itc.it • European project • Makes fun of existing • Humorous Agent for acronyms. Humourous Acronyms. ACM : • Acronym Ironic • Produces new acronyms •Re-analyzer We say: Association for and generator based on concepts provided Computing machinery by the user. • HAHA says: Association for Confusing machinery HAHAcronym : Concepts Synset • group of data elements that are considered semantically WordNet •A large database English.of equivalent for the of purposes information retrieval. WordNet Domains •Augment with •Words areWordNet grouped into sets • Eg. Person, Human, Individual domain labels. of synonyms (synsets), each expressing a distinct concept. •Example, the word ‘bank’ has two labelsare – interlinked by means •Synsets Economy and Geology. of semantic and lexical relations. HAHAcronym : Acronym modification Acronym parsing and construction of logical form Choice of what to modify and what to keep unchanged Substitutions 1. Using semantic field oppositions. Recognizes individual constituents such as NP, VP, etc. 2. Reproducing rhyme and rhythm. using acronym grammar. 3. Adjectives: antonym clustering and semantic relations in WordNet. HAHAcronym : Examples of Acr. Modification CCTT CHI •Close Combat Tactical Trainer •Close CombatHuman Theological Trainer Computer Interface. Computer Harry_Truman Interface. Two changes: antonym strategy for first adjective and semantic Unexpected result: opposition found in ‘religion' domain duefortotactical rhyming of "human" to theological. to "harry_truman" HAHAcronym : Acronym generation Input/Output Example Input: Main concept + Writing Concept: Attribute Attribute: Creative Output: A–new funny acronym CAUSTIC Creative Activity. for Unconvincingly Sporadically Talkative Individualistic Commercials Humour & AI Humour theory Computational Humour Humour Humour Recognition JAPE-1 Applications of Computational humour HAHAcronym Humour Recognition Experiment Strategies • • • Stylistic features Humorous data : One-liners Non-humorous : Content-based data features • Sentences from BNC • Sentences from • Combination of theReuters two • Proverbs Humour Recognition : Strategies Stylistic Features • Antonymy Content-based features ( A clean desk is a sign of cluttered drawer.) • Naïve Bayes • Uses joint probability of • Alliteration words and documents. ( Infants don’t enjoy infancy as much as adults do adultery.) Humour Recognition : Results Humour Recognition : N-grams Naïve Bayes assumes independent words. N-gram : • Allows a computer to process ‘word sequences’. • Uses conditional probability to predict word based on N-1 previous words. • Allows processing of ‘multi-words’. • Example: wild card, freak out, etc. Humour & AI Humour theory Computational Humour Humour Recognition JAPE-1 Applications Applications of of Computational humour HAHAcronym Applications • Advertisements • Human-Computer Interaction – Text-based Interface – Embodied Agents (Helpdesks) – Chat bots (A.L.I.C.E.) (Artificial Linguistic Internet Computer entity) PLEASE visit: www.alicebot.org Applications : ALICE Human: It’s a funny thing! ALICE: I thought it was too. Human: Then why didn’t you laugh? ALICE: Perhaps it is not part of my programming. Human: So you are not programmed to laugh at all? ALICE: Interesting deduction. Ha Ha. Applications : ALICE Human: Will you marry me? ALICE: Why don’t you just download me? Conclusion • Humour is a combination of intellect and emotions. It is subject to time, individual experience and perception. • Humour generation produces instances of humour. Textual humour is generated in systems like JAPE and HAHA. • Humour recognition takes help of machine learning techniques to understand the ‘humour’ content of a situation/statement. A conclusion is simply the place where you got tired of thinking. We learnt… The past was ‘computerization ’. The future is ‘ humanization ’. References Humour Theory and Computational Humour: www.dcs.gla.ac.uk/~kimb/dai_version/dai_version.html JAPE-1: • Kim Binsted and Graeme Ritchie. An implemented model of punning riddles. In Twelfth National Conference on Artificial Intelligence (AAAI94), pages 1-6, 1994. HAHAcronym: • An Experiment in Automated Humorous Output Production. Oliviero Stock and Carlo Strapparava. In IUT 2003, pages 1-3, 2003. Humour Recognition: • Making Computers Laugh. Rada Mihalcea and Carlo Strapparava. In Proceedings of HLT/EMNLP, pages 531-538, 2005. • www.wikipedia.org Humour & AI Questions? Comments? Suggestions? The past was computerization. The future is humanization.