Writing Stories with Help from Recurrent Neural Networks

By Melissa Roemmele. 2016

70 citations recorded by the source corpus.

This thesis explores the use of a recurrent neural network model for a novel story generation task. In this task, the model analyzes an ongoing story and generates a sentence that continues the story.

Subfields: Artificial Intelligence, Natural Language Processing, Generative Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Generative Models

Citations in the corpus

Hierarchical Neural Story Generation (2018, background): “Previous work on story generation has explored seq2seq RNN architectures (Roemmele, 2016), but has focused largely on using various content to inspire the stories.”
Unifying Human and Statistical Evaluation for Natural Language Generation (2019, background): “Generating text is a core part of many NLP tasks such as image captioning (Lin et al., 2014), open-domain dialogue (Sordoni et al., 2015), story generation (Roemmele, 2016), and summarization (Nallapati et al., 2016).”
Improved Natural Language Generation via Loss Truncation (2020, background): “Learning to generate text is a core part of many NLP tasks, including summarization (Nallapati et al., 2016), image captioning (Lin et al., 2014), and story generation (Roemmele, 2016).”
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning (2022, methodology): “Much of this work is based on prior work related to neural networks—recurrent and transformer-based—capable of producing stories (Roemmele, 2016; Khalifa et al., 2017; Martin et al., 2018; Clark et al., 2018; Fan et al., 2018; Ammanabrolu et al., 2021).”
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020, background): “While existing generative models (Roemmele, 2016; Fan et al., 2018, 2019) can generate stories with good local coherence, they are still struggling to plan a coherent plot and maintain a reasonable event sequence throughout the story, or they are often biased towards generating a limited set of…”
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning (2021, background): “edge engineering and tend to produce relatively fluent, varied, and naturalistic language (Roemmele, 2016; Khalifa et al., 2017; Clark et al., 2018; Martin et al., 2018).”
Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks (2021, methodology): “Roemmele and Gordon (Roemmele 2016) use a Long Short-Term Memory (LSTM) network (Hochreiter and Schmidhuber 1997) to generate stories.”