Mexico

A chatbot is perceived as more humanlike and likeable if it includes some jokes in its output. But most existing joke generators were not designed to be integrated into chatbots. This paper presents Witscript, a novel joke generation system that can improvise original, contextually relevant jokes, such as humorous responses during a conversation. The system is based on joke writing algorithms created by an expert comedy writer. Witscript employs well-known tools of natural language processing to extract keywords from a topic sentence and, using wordplay, to link those keywords and related words to create a punch line. Then a pretrained neural network language model that has been fine-tuned on a dataset of TV show monologue jokes is used to complete the joke response by filling the gap between the topic sentence and the punch line. A method of internal scoring filters out jokes that don&#39;t meet a preset standard of quality. Human evaluators judged Witscript&#39;s responses to input sentences to be jokes more than 40% of the time. This is evidence that Witscript represents an important next step toward giving a chatbot a humanlike sense of humor.

ICCC&#39;21

Witscript: A System for Generating Improvised Jokes in a
Conversation

comedy generation

humor generation

joke generation

computational humor

chatbot

conversational ai

natural language generation

natural language processing

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

A chatbot is perceived as more humanlike and likeable if it includes some jokes in its output. But most existing joke generators were not designed to be integrated into chatbots. This paper presents Witscript, a novel joke generation system that can improvise original, contextually relevant jokes, such as humorous responses during a conversation. The system is based on joke writing algorithms created by an expert comedy writer. Witscript employs well-known tools of natural language processing to extract keywords from a topic sentence and, using wordplay, to link those keywords and related words to create a punch line. Then a pretrained neural network language model that has been fine-tuned on a dataset of TV show monologue jokes is used to complete the joke response by filling the gap between the topic sentence and the punch line. A method of internal scoring filters out jokes that don't meet a preset standard of quality. Human evaluators judged Witscript's responses to input sentences to be jokes more than 40% of the time. This is evidence that Witscript represents an important next step toward giving a chatbot a humanlike sense of humor.

technical paper

### **Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!**

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Computational creativity is an interdisciplinary endeavor that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, social sciences and the arts. The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:

* to construct a program or computer capable of showing some level of creativity
* to better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans
* to design programs that can enhance human creativity without necessarily being creative themselves

<br>The ICCC conference series organized by the [**Association for Computational Creativity**](https://computationalcreativity.net/) since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.



To access the Twelfth International Conference on Computational Creativity, ICCC’21, you need to register for the conference.  Instructions on how to access the event will be provided directly to attendees and included on the Twelfth International Conference on Computational Creativity, ICCC’21 conference website. 

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ICCC'21

Welcome to the Twelfth International Conference on Computational Creativity, ICCC’21!

ML Design for Creative Pursuits

We present a Computational Creativity system based on Conceptual Blending theory. To obtain diverse output -- in the form of blend spaces -- it allows a trade-off in the optimisation process according to multiple criteria. It is handled by a high performance Multiple Objective Evolutionary Algorithm supporting a large Knowledge Base, large input spaces, a high number of mappings as well of frames, all in the range of millions of elements in their structures. Some of CB's Optimality Principles were adapted into objectives as well as new ideas we decided to investigate. Initial experiments allows us to conclude that the system exhibits a form of creativity and its output is capable of depict a simple drawing or a short story.

A Heavy Duty Computational Model of Conceptual Blending Using Multiple Objective Optimisation

Wölfflin Affective Generative Analysis (WAGA) as an approach to understand and analyze the progress of machine-generated artworks in contrast to real art and their connection to our human artistic heritage, and how they extend the shape of art history. Specifically, we studied the machine-generated art after integrating creativity losses in the state-of-the-art generative models e.g., StyleGAN v1 and v2. We denote these models as Style Creative Adversarial Networks v1 and v2; in short, StyleCAN v1 and v2. We contrasted the learned representation between real and generated artworks through correlation analysis between constructed emotion (collected through Amazon MTurk) and Heinrich Wölfflin (1846-1945)'s principles of art history. Analogous to the recent ArtEmis dataset, we collected constructed emotions and explanations on generated art instead of real art to study the contrast. To enable Wölfflin Affective Generative Analysis, we collected 45,000 annotations on 1800 artworks; 1000 real and 800 generated. Our analysis shows a correlation exists between the Wölfflin principles and the emotions.

Wölfflin's Affective Generative Analysis of Artworks

MuSyFI is a system that tries to model an inspirational computational creative process. It uses images as source of inspiration and begins by implementing a possible translation between visual and musical features. Three different musical artifacts are generated: an automatic version, a co-created version, and a genetic version. The automatic version maps features from the image into musical features nondeterministically; the co-created version adds harmony lines manually composed by us to the automatic version; finally, the genetic version applies a genetic algorithm to a mixed population of automatic and co-created artifacts. In this Demo, we present the three different versions for four of the six evaluated images in our surveys

MuSyFI - Music Synthesis From Images

SOVIA, an interactive system that endows Claude Monet’s art with responsive auditory experiences. SOVIA uses computer vision trained on Monet’s artwork to take the user ``into the painting.'' When the user interacts with a digital version of Monet's landscapes, their mouse positions are mapped to sounds that artistically represent the objects that the user is currently exploring in the art. These interactive musical journeys have the potential to make classical art more captivating for modern audiences.

SOVIA: Sonification of Visual Interactive Art

In this short paper, we reflect on the long quest for intelligence and creativity of computing machinery as well as the suitability for prevailing machine learning techniques to be used in creative tasks. We believe that modularization and multi-layered structures are among essential ingredients constituting creative minds and may greatly benefit machines on handling creative tasks. For proof of concept, we select musical composition, particularly, Species Counterpoint, as the task, adopt a recently proposed computational framework designed for investigating creativity and the creative process, and present an implementation capable of producing the outcomes that exhibit the desired effect.

In the Name of Creativity: En Route to Inspiring Machines

We present a proof-of-concept prototype aimed at providing a subjectivity touch to the production of creative artifacts. The underlying idea is to have a system in which the development of the artifact occurs concurrently with the dynamic formation of a personal identity, thus enriching the overall perception of creativity.

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