10 citations · 14 across the 3 of their papers we have counts for
5 papers · 1 filter
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models
Matteo Alleman, Jonathan Mamou, Miguel A Del Rio +3
While vector-based language representations from pretrained language models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workin…
Parameter-Efficient Transfer Learning with Diff Pruning
Demi Guo, Alexander M. Rush, Yoon Kim
While task-specific finetuning of pretrained networks has led to significant empirical advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-tas…
Sequence-Level Mixed Sample Data Augmentation
Demi Guo, Yoon Kim, Alexander M. Rush
Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach t…
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou, Hang Le, Miguel Del Rio +4
Deep neural networks (DNNs) have shown much empirical success in solving perceptual tasks across various cognitive modalities. While they are only loosely inspired by the biologica…
OpenNMT: Open-source Toolkit for Neural Machine Translation
Guillaume Klein, Yoon Kim, Yuntian Deng +3
We introduce an open-source toolkit for neural machine translation (NMT) to support research into model architectures, feature representations, and source modalities, while maintai…