1 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
Sensitivity as a Complexity Measure for Sequence Classification Tasks
Michael Hahn, Dan Jurafsky, Richard Futrell
We introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sen…
RNNs can generate bounded hierarchical languages with optimal memory
John Hewitt, Michael Hahn, Surya Ganguli +2
Recurrent neural networks empirically generate natural language with high syntactic fidelity. However, their success is not well-understood theoretically. We provide theoretical in…
Tabula nearly rasa: Probing the Linguistic Knowledge of Character-Level Neural Language Models Trained on Unsegmented Text
Michael Hahn, Marco Baroni
Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing…
Character-based Surprisal as a Model of Reading Difficulty in the Presence of Error
Michael Hahn, Frank Keller, Yonatan Bisk +1
Intuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter…