Recent advances in neural-symbolic learning, such as DeepProbLog, extend probabilistic logic programs with neural predicates. Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard. We propose DeepStochLog, an alternative neural-symbolic framework based on stochastic definite clause grammars, a kind of stochastic logic program. More specifically, we introduce neural grammar rules into stochastic definite clause grammars to create a framework that can be trained end-to-end. We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs. Furthermore, the experimental evaluation shows that DeepStochLog achieves state-of-the-art results on challenging neural-symbolic learning tasks.

DeepStochLog: Neural Stochastic Logic Programming

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

Designers increasingly rely on procedural generation for automatic generation of content in various industries. These techniques require extensive knowledge of the desired content, and about how to actually implement such procedural methods. Algorithms for learning interpretable generative models from example content could alleviate both difficulties. We propose SIGI, a novel method for inferring shapes and inducing a shape grammar from grid-based 3D building examples. This interpretable grammar is well-suited for co-creative design. Applied to Minecraft buildings, we show how the shape grammar can be used to automatically generate new buildings in a similar style.

Shape Inference and Grammar Induction for Example-based Procedural Generation

Portmanteaus are a type of neologism combining two source words, for example brunch, and are popular for naming all kinds of phenomena. While coming up with suitable portmanteaus is a difficult creative endeavor, several portmanteau generators already exist for assistance in this process. When using these systems, it is often hard to find out which of the generated portmanteau is likely to be the best, and consequently also hard to automatically compare the quality of different portmanteau generators. In this paper, we create a model that can rank portmanteaus for two given source words, which thus aims to help find the best portmanteau to help further improve portmanteau generators. Our model first uses XGBoost trained on unlabeled generated outputs and existing portmanteaus to learn to rank portmanteaus and shows that this already greatly improves the performance of the initial generator. By ranking outputs of a state-of-the-art generator and a new simple portmanteau generator, we show by validating its quality in a human evaluation that the ranker can help visually identify the better generator, thus providing an alternative to only calculating real portmanteau generation frequency. Additionally, we find that this first model performs almost as well as a model trained on more fine-grained human-labeled portmanteaus. This indicates that just using generated and real portmanteaus is enough to create a ranker that can in turn improve the quality of the initial generator, and could additionally be of use in comparing different portmanteau generators.

Learning to Rank Generated Portmanteaus

Main_Track

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

**Title:** Survival of the Wittiest: Evolving Satire with Language Models 
**Presenters:** Thomas Winters and Pieter Delobelle 
**Title:** Assessing MultiPlot Stories: from Formative Analysis to Computational Metrics 
**Presenters:** Pablo Gervás, Eugenio Concepcion and Gonzalo Mendez 
**Title:** Collaborative Storytelling with Human Actors and AI Narrators 
**Presenters:** Boyd Branch, Piotr Mirowski and Kory Mathewson 
**Title:** Evaluating Natural Language Descriptions Generated in a Workspace-Based Architecture 
**Presenters:** George Wright and Matthew Purver

Linguistic Creativity III 

### **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:

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* to design programs that can enhance human creativity without necessarily being creative themselves

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ICCC'21

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

Thomas Winters

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Presentations

DeepStochLog: Neural Stochastic Logic Programming

Survival of the Wittiest: Evolving Satire with Language Models

Shape Inference and Grammar Induction for Example-based Procedural Generation

Learning to Rank Generated Portmanteaus

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