104 citations · 114 across the 5 of their papers we have counts for
5 papers
On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Yonatan Belinkov, Adam Poliak, Stuart M. Shieber +2
Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious…
Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
Yonatan Belinkov, Adam Poliak, Stuart M. Shieber +2
Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise e…
Tensor Variable Elimination for Plated Factor Graphs
Fritz Obermeyer, Eli Bingham, Martin Jankowiak +4
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do n…
Lie-Access Neural Turing Machines
Greg Yang, Alexander M. Rush
External neural memory structures have recently become a popular tool for algorithmic deep learning (Graves et al. 2014, Weston et al. 2014). These models generally utilize differe…
A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing
Alexander M. Rush, Michael Collins
Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in na…