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04/27/2023
Controlled Text Generation with Natural Language Instructions
Large language models generate fluent texts and can follow natural langu...
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01/27/2023
Call for Papers – The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus
We present the call for papers for the BabyLM Challenge: Sample-efficien...
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11/04/2020
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
Previous studies investigating the syntactic abilities of deep learning ...
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10/12/2020
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
Humans can learn structural properties about a word from minimal experie...
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05/07/2020
A Systematic Assessment of Syntactic Generalization in Neural Language Models
State-of-the-art neural network models have achieved dizzyingly low perp...
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09/10/2019
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
Neural language models have achieved state-of-the-art performances on ma...
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06/10/2019
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
Deep learning sequence models have led to a marked increase in performan...
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05/24/2019
What Syntactic Structures block Dependencies in RNN Language Models?
Recurrent Neural Networks (RNNs) trained on a language modeling task hav...
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03/08/2019
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
We deploy the methods of controlled psycholinguistic experimentation to ...
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03/03/2019
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
State-of-the-art LSTM language models trained on large corpora learn seq...
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09/05/2018
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency
Recurrent neural networks (RNNs) are the state of the art in sequence mo...
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08/31/2018
