A model of suspense for narrative generation

Most work on automatic generation of narratives, and more specifically suspenseful narrative, has focused on detailed domain-specific modelling of character psychology and plot structure. Recent work on the automatic learning of narrative schemas suggests an alternative approach that exploits such schemas for modelling and measuring suspense. We propose a domain-independent model for tracking suspense in a story which can be used to predict the audience’s suspense response on a sentence-by-sentence basis at the content determination stage of narrative generation. The model lends itself as the theoretical foundation for a suspense module that is compatible with alternative narrative generation theories. The proposal is evaluated by human judges’ normalised average scores correlate strongly with predicted values

Controllable Neural Story Plot Generation via Reinforcement Learning

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model towards a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.Comment: Published in IJCAI 201

El castellano que usan los escolares de Cataluña

Los alumnos y alumnas de un centro barcelonés que se relacionan en castellano conocen a los de dos escuelas que usan el catalán en su vida cotidiana, lo cual no les impide aprender castellano en las clases y en otras interacciones sociales. Frente a la idea de que la enseñanza en catalán merma el aprendizaje del castellano, esta experiencia demuestra que unos y otros lo saben usar

Learning to Create Jazz Melodies Using Deep Belief Nets

We describe an unsupervised learning technique to facilitate automated creation of jazz melodic improvisation over chord sequences. Specifically we demonstrate training an artificial improvisation algorithm based on unsupervised learning using deep belief nets, a form of probabilistic neural network based on restricted Boltzmann machines. We present a musical encoding scheme and specifics of a learning and creational method. Our approach creates novel jazz licks, albeit not yet in real-time. The present work should be regarded as a feasibility study to determine whether such networks could be used at all. We do not claim superiority of this approach for pragmatically creating jazz

Report on the eighth international conference on computational creativity

The Eighth International Conference on Computational Creativity (ICCC’17)1 was hosted at the Georgia Institute of Technology in Atlanta, Georgia, USA from June 19th - June 23rd, 2017. The ICCC’17 organising committee consisted of Ashok Goel (General Chair), Kazjon Grace (Workshop Co-chair), Matthew Guzdial (Media Chair), Mikhail Jacob (Local Chair), Anna Jordanous (Program Co-chair), Ruli Manurung (Workshop Co-chair) and Alison Pease (Program Co-chair). This report summarises the main topics addressed

Boletín oficial de la provincia de León: Num. 275 (07/12/1938)

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2011-201