3 citations · 9 across the 7 of their papers we have counts for
9 papers
Can Optimization Trajectories Explain Multi-Task Transfer?
David Mueller, Mark Dredze, Nicholas Andrews
Despite the widespread adoption of multi-task training in deep learning, little is understood about how multi-task learning (MTL) affects generalization. Prior work has conjectured…
Can Authorship Representation Learning Capture Stylistic Features?
Andrew Wang, Cristina Aggazzotti, Rebecca Kotula +3
Automatically disentangling an author's style from the content of their writing is a longstanding and possibly insurmountable problem in computational linguistics. At the same time…
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
David Mueller, Nicholas Andrews, Mark Dredze
Traditional multi-task learning architectures train a single model across multiple tasks through a shared encoder followed by task-specific decoders. Learning these models often re…
Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?
Ajay Patel, Nicholas Andrews, Chris Callison-Burch
Authorship style transfer involves altering text to match the style of a target author whilst preserving the original meaning. Existing unsupervised approaches like STRAP have larg…
A Deep Metric Learning Approach to Account Linking
Aleem Khan, Elizabeth Fleming, Noah Schofield +2
We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and metadata of their corresponding documen…
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three
Steven Reich, David Mueller, Nicholas Andrews
Modern neural networks do not always produce well-calibrated predictions, even when trained with a proper scoring function such as cross-entropy. In classification settings, simple…
Sources of Transfer in Multilingual Named Entity Recognition
David Mueller, Nicholas Andrews, Mark Dredze
Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one m…
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
Mitchell A. Gordon, Kevin Duh, Nicholas Andrews
Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models…
Learning Invariant Representations of Social Media Users
Nicholas Andrews, Marcus Bishop
The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, naïve ap…