80 citations · 209 across the 33 of their papers we have counts for
12 papers · 1 filter
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning
Amal Alabdulkarim, Winston Li, Lara J. Martin +1
The advent of large pre-trained generative language models has provided a common framework for AI story generation via sampling the model to create sequences that continue the stor…
Automated Story Generation as Question-Answering
Louis Castricato, Spencer Frazier, Jonathan Balloch +2
Neural language model-based approaches to automated story generation suffer from two important limitations. First, language model-based story generators generally do not work towar…
Inherently Explainable Reinforcement Learning in Natural Language
Xiangyu Peng, Mark O. Riedl, Prithviraj Ammanabrolu
We focus on the task of creating a reinforcement learning agent that is inherently explainable -- with the ability to produce immediate local explanations by thinking out loud whil…
Reframing Human-AI Collaboration for Generating Free-Text Explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta +2
Large language models are increasingly capable of generating fluent-appearing text with relatively little task-specific supervision. But can these models accurately explain classif…
Guiding Neural Story Generation with Reader Models
Xiangyu Peng, Kaige Xie, Amal Alabdulkarim +3
Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-…
Situated Dialogue Learning through Procedural Environment Generation
Prithviraj Ammanabrolu, Renee Jia, Mark O. Riedl
We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al. 2019) -- a lar…
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts
Ashutosh Baheti, Maarten Sap, Alan Ritter +1
Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also impl…
Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning
Xiangyu Peng, Siyan Li, Sarah Wiegreffe +1
Transformer-based language model approaches to automated story generation currently provide state-of-the-art results. However, they still suffer from plot incoherence when generati…
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior
Md Sultan Al Nahian, Spencer Frazier, Brent Harrison +1
As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally, using only a measure of task performance as f…
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Ronal Singh, Upol Ehsan, Marc Cheong +2
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally. In this position paper, we define a framework called the…
Fabula Entropy Indexing: Objective Measures of Story Coherence
Louis Castricato, Spencer Frazier, Jonathan Balloch +1
Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer…
Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes
Zhiyu Lin, Mark Riedl
Large pre-trained neural language models (LM) have very powerful text generation capabilities. However, in practice, they are hard to control for creative purposes. We describe a P…