Search Results for author:
Found 6 papers, 2 papers with code
Experiential Explanations for Reinforcement Learning
However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen.
Guiding Neural Story Generation with Reader Models
In this paper, we introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress.
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning
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 story.
AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages.
Automatic Story Generation: Challenges and Attempts
The scope of this survey paper is to explore the challenges in automatic story generation.
Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News Detection
This paper describes our system for detecting hyperpartisan news articles, which was submitted for the shared task in SemEval 2019 on Hyperpartisan News Detection.
