Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions

no code implementations • 12 Mar 2021 • Pallika Kanani, Virendra J. Marathe, Daniel Peterson, Rave Harpaz, Steve Bright

Users can indirectly contribute to, and directly benefit from a much larger aggregate data corpus used to train the global model.

Event Detection Federated Learning +3

Private Federated Learning with Domain Adaptation

no code implementations • 13 Dec 2019 • Daniel Peterson, Pallika Kanani, Virendra J. Marathe

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy.

BIG-bench Machine Learning Domain Adaptation +1

Leveraging VerbNet to build Corpus-Specific Verb Clusters

no code implementations • SEMEVAL 2016 • Daniel Peterson, Jordan Boyd-Graber, Martha Palmer, Daisuke Kawahara

Semantic Role Labeling

Focusing Annotation for Semantic Role Labeling

no code implementations • LREC 2014 • Daniel Peterson, Martha Palmer, Shumin Wu

We show that the least probable sentences provide dramatic improved system performance over the baseline, especially when only a small portion of the data is annotated.

Active Learning Domain Adaptation +3

Inducing Example-based Semantic Frames from a Massive Amount of Verb Uses

no code implementations • EACL 2014 • Daisuke Kawahara, Daniel Peterson, Octavian Popescu, Martha Palmer

Machine Translation Natural Language Inference +2