100 citations · 183 across the 6 of their papers we have counts for
11 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…
GLTR: Statistical Detection and Visualization of Generated Text
Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush
The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generat…
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
Brandon Reagen, Udit Gupta, Robert Adolf +4
The large memory requirements of deep neural networks limit their deployment and adoption on many devices. Model compression methods effectively reduce the memory requirements of t…
Dilated Convolutions for Modeling Long-Distance Genomic Dependencies
Ankit Gupta, Alexander M. Rush
We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-d…
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…