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The ability to produce surprising outputs is a cornerstone of creative behavior. In this paper we propose deep learning architectures that are trained in an adversarial setting to learn patterns of expectations and surprise from data. We introduce benchmark datasets of geometrical shapes that represent well defined patterns of violations of expectations, and use them to verify empirically that the separation between learning of expectations and learning of surprise is essential for achieving good generalization performance.

ICCC&#39;21

Adversarial Learning of Expectation and Surprise: Experiments with Geometric Shapes

surprise

creativity

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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. 

[Register now](https://computationalcreativity.net/iccc21/registration/)

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ICCC'21

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

In this presentation we focus on two of the major concepts we present in the main paper, namely: how we can understand the Typicality and Novelty of an object with Models created by an observer, and sophistication which lets us analyse the creator of a creative object. We also talk about highly sophisticated objects which can be regarded as masterpieces of the creator. Finally, we review some related works in computational aesthetics and how they can be improved. 



Incorporating Algorithmic Information Theory into Fundamental Concepts of Computational Creativity

Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results. In the context of computational creativity, however, a major shortcoming is that they are unable to explicitly diverge from the training data in creative ways and are limited to fitting the target data distribution. To address these limitations, there have been a growing number of approaches for optimising, hacking and rewriting these models in order to actively diverge from the training data. We present a taxonomy and comprehensive survey of the state of the art of active divergence techniques, highlighting the potential for computational creativity researchers to advance these methods and use deep generative models in truly creative systems.

Active Divergence with Generative Deep Learning - A Survey and Taxonomy

Getting machines to generate text perceived as creative is a long-pursued goal. A growing body of research directs this goal towards augmenting the creative writing abilities of human authors. In this paper, we pursue this objective by analyzing how observing examples of automatically generated text influences writing. In particular, we examine a task referred to as sentence infilling, which involves transforming a list of words into a complete sentence. We emphasize “storiability” as a desirable feature of the resulting sentences, where “storiable” sentences are those that suggest a story a reader would be curious to hear about. Both humans and an automated system (based on a neural language model) performed this sentence infilling task. In one setting, people wrote sentences on their own; in a different setting, people observed the sentences produced by the model while writing their own sentences. Readers then assigned storiability preferences to the resulting sentences in a subsequent evaluation. We find that human-authored sentences were judged as more storiable when authors observed the generated examples, and that storiability increased as authors derived more semantic content from the examples. This result gives evidence of an “inspiration through observation” paradigm for human-computer collaborative writing, through which human writing can be enhanced by text generation models without directly copying their output.

Inspiration through Observation: Demonstrating the Influence of Automatically Generated Text on Creative Writing

Large pre-trained transformer-based language models have revolutionized the field of natural language processing in recent years. While BERT-like models perform exceptionally well for analytical tasks such as classification and regression, their text generation capabilities are usually limited to predicting tokens within a given context. In this paper, we introduce Galmet, a model that generates text by using genetic algorithms with BERT-like language models for evolving text. We use Galmet with the RoBERTa language model to automatically evolve real headlines into more satirical headlines. This is achieved by adapting the masked language head to the headlines domain for the mutation operator and finetuning a regression head to distinguish headlines from satire for the fitness function. We evaluated our system by comparing generated satirical headlines against human-edited headlines and just the fine-tuned masked language head. We found that while humans generally outperform the model, generations by Galmet are also often preferred over human-edited headlines. However, we also found that only using the fine-tuned masked language model gives slightly preferred satire due to generating more readable sentences. Galmet is thus a first step towards a new way of creating text generators using masked language models by transforming text guided by scores from another language model.

Survival of the Wittiest: Evolving Satire with Language Models

Recent interest in story generators capable of combining more than on plot line into an elaborate story have been handicapped by the lack of either theoretical material or quantitative metrics to ascertain the quality of outputs of such attempts. The present short paper postulates a set of metrics designed to capture some of the insights elaborated during a formative evaluation of an existing attempt at plot weaving.

Assessing MultiPlot Stories: from Formative Analysis to Computational Metrics

This paper concerns the evaluation of a workspace architecture for generating natural language descriptions, including methods for evaluating both its output and its own self-evaluation. Herein are details of preliminary results from evaluation of an early iteration of the architecture operating in the domain of weather. The domain is not typically seen as creative, but provides a simple testbed for the architecture and evaluation methodology. The program does not yet match humans in terms of fluency of language, factual correctness, and how completely the input is described, but human judges did find the program's output easier to read than human generated texts. Planned improvements to the program also described in the paper will incorporate self-monitoring and better self-evaluation with the aim of producing descriptions that are more fluently written and more accurate.

Evaluating Natural Language Descriptions Generated in a Workspace-Based Architecture

RoboCHAIR is a system for generating scientific questions and is implemented as a web interface. We focus on generating questions which can trigger new scientific
ideas or bring attention to the elements that need clarifications. This paper extends the initial version of the RoboCHAIR’s question generation module by keeping only a selection of best evaluated templates from the initial pattern-based approach and proposes a novel method based on triplet graphs enriched with word embeddings
to identify parts of text which require clarifications by the author(s). In this paper the two methods are compared showing that the pattern-based method achieves higher scores.

Scientific question generation: pattern-based and graph-based RoboCHAIR methods

Social media is now the megaphone of choice for many digital-age provocateurs. Social networks offer a wealth of examples of egregious misbehaviour by humans, but perhaps more worryingly, they also serve to magnify and weaponize the automated outputs of generative systems. So what responsibilities do the creators of these systems bear for the offenses caused by their creations, and what can they do to prevent, or mitigate, the worst excesses? For those who “make things that make things,” the issue is further complicated by the potential of our systems to accidentally tread on the feelings of others. When does a norm become a troubling stereotype, and how should a system built on social norms avoid crossing the line? We argue that because this line is so easy to cross, our systems should aim to engage with and mitigate offense rather than try vainly to prevent it completely.


Walking the Line in Computational Creativity: Paradox and Pitfall in the Mitigation of Automated Offense

Although creativity is an ability that can be developed if trained, most of the systems developed for creativity stimulation focused on individual interventions. This work presents the design, development, and evaluation of CUBUS, a digital tool developed to inspire social creativity in children through storytelling and emotionally expressive characters. Groups of children collaborate with the autonomous virtual characters of the digital tool to create a story together. Our main goal was to conduct an experimental study (n=20) to investigate if the interaction between children and the autonomous emotionally expressive virtual characters present in CUBUS can stimulate social creativity during a storytelling activity. We measured the impact of the digital tool across several creativity domains: pre-and post-testing (questionnaire assessment), the creative process of children (behavior analysis), and the story created (creative outcome). Results showed that although children generated fewer storytelling ideas using CUBUS, these ideas were deemed more original.

Inspiring Social Creativity in Children with a Digital Tool

What if creative machines could show the story told by a live orchestra? In this paper, we propose a vision for a co-creative orchestra performance partner that enlivens this classical art form and has the potential to increase audience engagement and reach broader audiences. We present MIST, an early version of our vision, which creates real-time visualizations as live music is performed, and showcase it in a violin performance.

Adversarial Learning of Expectation and Surprise: Experiments with Geometric Shapes

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