research
∙
06/06/2023
A Cross-Linguistic Pressure for Uniform Information Density in Word Order
While natural languages differ widely in both canonical word order and w...
share
research
∙
12/16/2022
A unified information-theoretic model of EEG signatures of human language processing
We advance an information-theoretic model of human language processing i...
research
∙
03/11/2022
When classifying grammatical role, BERT doesn't care about word order... except when it matters
Because meaning can often be inferred from lexical semantics alone, word...
research
∙
01/30/2022
Grammatical cues are largely, but not completely, redundant with word meanings in natural language
The combinatorial power of language has historically been argued to be e...
research
∙
04/21/2021
Sensitivity as a Complexity Measure for Sequence Classification Tasks
We introduce a theoretical framework for understanding and predicting th...
research
∙
01/26/2021
Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT
We investigate how Multilingual BERT (mBERT) encodes grammar by examinin...
research
∙
12/30/2020
Predicting cross-linguistic adjective order with information gain
Languages vary in their placement of multiple adjectives before, after, ...
research
∙
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...
research
∙
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...
research
∙
05/24/2019
What Syntactic Structures block Dependencies in RNN Language Models?
Recurrent Neural Networks (RNNs) trained on a language modeling task hav...
research
∙
03/08/2019
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic State
We deploy the methods of controlled psycholinguistic experimentation to ...
research
∙
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...
research
∙
11/05/2018
Do RNNs learn human-like abstract word order preferences?
RNN language models have achieved state-of-the-art results on various ta...
research
∙
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...
research
∙
08/31/2018
What do RNN Language Models Learn about Filler-Gap Dependencies?
RNN language models have achieved state-of-the-art perplexity results an...
research
∙
09/08/2017
A Statistical Comparison of Some Theories of NP Word Order
A frequent object of study in linguistic typology is the order of elemen...
research
∙
08/18/2017
The Natural Stories Corpus
It is now a common practice to compare models of human language processi...
research
∙
10/01/2015
