NEURAL CONTROL

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Papers with neural control

NAACL

N19-1

LSTM language models
hierarchical structure enhances learning
controlled experimental methods
word-based LSTM models
Recurrent Neural Network Grammars
RNNGs
neural control
RNNG
LSTM
string-based training of neural language models

2019

Language Models

1 papers

Structural Supervision Improves Learning of Non-Local Grammatical Dependencies (N19-1)

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Ethan Wilcox, Peng Qian, Richard Futrell, Miguel Ballesteros, Roger Levy

Language Models

Challenge:	State-of-the-art LSTM language models learn sequential contingencies with some success . LS models fail to learn other non-local grammatical dependencies, however .
Approach:	They compare LSTM language models with RNNGs to examine grammatical dependencies . they find that hierarchical supervision improves learning of non-local dependencies.
Outcome:	The proposed model outperforms the existing model on non-local dependencies and learns many of the Island Constraints on the filler-gap dependency.

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