565 citations · 939 across the 21 of their papers we have counts for
4 papers · 1 filter
Memory-Augmented Recurrent Neural Networks Can Learn Generalized Dyck Languages
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov +1
We introduce three memory-augmented Recurrent Neural Networks (MARNNs) and explore their capabilities on a series of simple language modeling tasks whose solutions require stack-ba…
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…
LSTM Networks Can Perform Dynamic Counting
Mirac Suzgun, Sebastian Gehrmann, Yonatan Belinkov +1
In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in…