O
Orion Weller
Researcher at Brigham Young University
Publications - 22
Citations - 251
Orion Weller is an academic researcher from Brigham Young University. The author has contributed to research in topics: Computer science & Joke. The author has an hindex of 4, co-authored 12 publications receiving 110 citations. Previous affiliations of Orion Weller include Johns Hopkins University.
Papers
More filters
Proceedings ArticleDOI
Humor Detection: A Transformer Gets the Last Laugh
Orion Weller,Kevin D. Seppi +1 more
TL;DR: This paper proposed a Transformer-based model to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances, using these ratings to determine the level of humor.
Proceedings ArticleDOI
Learning from Task Descriptions
TL;DR: This work introduces a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area, and instantiates it with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks.
Posted Content
Humor Detection: A Transformer Gets the Last Laugh
Orion Weller,Kevin D. Seppi +1 more
TL;DR: This paper builds a model that learns to identify humorous jokes based on ratings gleaned from Reddit pages, consisting of almost 16,000 labeled instances, and employs a Transformer architecture for its advantages in learning from sentence context.
Proceedings Article
The rJokes Dataset: a Large Scale Humor Collection
Orion Weller,Kevin D. Seppi +1 more
TL;DR: A collection of over 550,000 jokes posted over an 11 year period on the Reddit r/Jokes subreddit, providing a large scale humor dataset that can be used for a myriad of tasks and introducing this dataset as a task for future work, where models learn to predict the level of humor in a joke.
Proceedings ArticleDOI
Pretrained Models for Multilingual Federated Learning
TL;DR: The results show that using pretrained models reduces the negative effects of FL, helping them to perform near or better than centralized (no privacy) learning, even when using non-IID partitioning.