565 citations · 818 across the 32 of their papers we have counts for
4 papers · 1 filter
Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer
Richard Yuanzhe Pang, Kevin Gimpel
We consider the problem of automatically generating textual paraphrases with modified attributes or properties, focusing on the setting without parallel data (Hu et al., 2017; Shen…
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Mohit Iyyer, John Wieting, Kevin Gimpel +1
We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency pa…
Learning Approximate Inference Networks for Structured Prediction
Lifu Tu, Kevin Gimpel
Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among pa…
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson +1
The growing importance of massive datasets used for deep learning makes robustness to label noise a critical property for classifiers to have. Sources of label noise include automa…