Collaborative Storytelling with Large-scale Neural Language Models
- URL: http://arxiv.org/abs/2011.10208v1
- Date: Fri, 20 Nov 2020 04:36:54 GMT
- Title: Collaborative Storytelling with Large-scale Neural Language Models
- Authors: Eric Nichols and Leo Gao and Randy Gomez
- Abstract summary: We introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique story by taking turns adding to it.
We present a collaborative storytelling system which works with a human storyteller to create a story by generating new utterances based on the story so far.
- Score: 6.0794985566317425
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Storytelling plays a central role in human socializing and entertainment.
However, much of the research on automatic storytelling generation assumes that
stories will be generated by an agent without any human interaction. In this
paper, we introduce the task of collaborative storytelling, where an artificial
intelligence agent and a person collaborate to create a unique story by taking
turns adding to it. We present a collaborative storytelling system which works
with a human storyteller to create a story by generating new utterances based
on the story so far. We constructed the storytelling system by tuning a
publicly-available large scale language model on a dataset of writing prompts
and their accompanying fictional works. We identify generating sufficiently
human-like utterances to be an important technical issue and propose a
sample-and-rank approach to improve utterance quality. Quantitative evaluation
shows that our approach outperforms a baseline, and we present qualitative
evaluation of our system's capabilities.
Related papers
- Are Large Language Models Capable of Generating Human-Level Narratives? [114.34140090869175]
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression.
We introduce a novel computational framework to analyze narratives through three discourse-level aspects.
We show that explicit integration of discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling.
arXiv Detail & Related papers (2024-07-18T08:02:49Z) - SARD: A Human-AI Collaborative Story Generation [0.0]
We propose SARD, a drag-and-drop visual interface for generating a multi-chapter story using large language models.
Our evaluation of the usability of SARD and its creativity support shows that while node-based visualization of the narrative may help writers build a mental model, it exerts unnecessary mental overhead to the writer.
We also found that AI generates stories that are less lexically diverse, irrespective of the complexity of the story.
arXiv Detail & Related papers (2024-03-03T17:48:42Z) - Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion
Models [70.86603627188519]
We focus on a novel, yet challenging task of generating a coherent image sequence based on a given storyline, denoted as open-ended visual storytelling.
We propose a learning-based auto-regressive image generation model, termed as StoryGen, with a novel vision-language context module.
We show StoryGen can generalize to unseen characters without any optimization, and generate image sequences with coherent content and consistent character.
arXiv Detail & Related papers (2023-06-01T17:58:50Z) - The Next Chapter: A Study of Large Language Models in Storytelling [51.338324023617034]
The application of prompt-based learning with large language models (LLMs) has exhibited remarkable performance in diverse natural language processing (NLP) tasks.
This paper conducts a comprehensive investigation, utilizing both automatic and human evaluation, to compare the story generation capacity of LLMs with recent models.
The results demonstrate that LLMs generate stories of significantly higher quality compared to other story generation models.
arXiv Detail & Related papers (2023-01-24T02:44:02Z) - A Benchmark for Understanding and Generating Dialogue between Characters
in Stories [75.29466820496913]
We present the first study to explore whether machines can understand and generate dialogue in stories.
We propose two new tasks including Masked Dialogue Generation and Dialogue Speaker Recognition.
We show the difficulty of the proposed tasks by testing existing models with automatic and manual evaluation on DialStory.
arXiv Detail & Related papers (2022-09-18T10:19:04Z) - Computational Storytelling and Emotions: A Survey [56.95572957863576]
This survey paper is intended to summarize and contribute to the development of research being conducted on the relationship between stories and emotions.
We believe creativity research is not to replace humans with computers, but to find a way of collaboration between humans and computers to enhance the creativity.
arXiv Detail & Related papers (2022-05-23T00:21:59Z) - Guiding Neural Story Generation with Reader Models [5.935317028008691]
We introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress.
Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.
arXiv Detail & Related papers (2021-12-16T03:44:01Z) - A guided journey through non-interactive automatic story generation [0.0]
The article presents requirements for creative systems, three types of models of creativity (computational, socio-cultural, and individual), and models of human creative writing.
The article concludes that the autonomous generation and adoption of the main idea to be conveyed and the autonomous design of the creativity ensuring criteria are possibly two of most important topics for future research.
arXiv Detail & Related papers (2021-10-08T10:01:36Z) - Stylized Story Generation with Style-Guided Planning [38.791298336259146]
We propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context.
Our model can controllably generateemo-tion-driven or event-driven stories based on the ROCStories dataset.
arXiv Detail & Related papers (2021-05-18T15:55:38Z) - Cue Me In: Content-Inducing Approaches to Interactive Story Generation [74.09575609958743]
We focus on the task of interactive story generation, where the user provides the model mid-level sentence abstractions.
We present two content-inducing approaches to effectively incorporate this additional information.
Experimental results from both automatic and human evaluations show that these methods produce more topically coherent and personalized stories.
arXiv Detail & Related papers (2020-10-20T00:36:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.