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1
Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity ...
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2
Do Language Models Learn Position-Role Mappings? ...
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3
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models ...
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4
Arguments for top-down derivations in syntax
In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5264 ; 2473-8689 (2022)
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5
Structure Here, Bias There: Hierarchical Generalization by Jointly Learning Syntactic Transformations
In: Proceedings of the Society for Computation in Linguistics (2021)
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6
Comparing methods of tree-construction across mildly context-sensitive formalisms
In: Proceedings of the Society for Computation in Linguistics (2021)
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7
The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions ...
Srivastava, Aarohi; Frank, Robert; Widder, Sarah. - : University of Mass Amherst, 2020
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8
Sequence-to-Sequence Networks Learn the Meaning of Reflexive Anaphora ...
Frank, Robert; Petty, Jackson. - : arXiv, 2020
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9
Sequence-to-Sequence Networks Learn the Meaning of Reflexive Anaphora ...
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10
Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling ...
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11
The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions
In: Proceedings of the Society for Computation in Linguistics (2020)
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12
Primitive Asymmetric C-Command Derives X̄-Theory
In: North East Linguistics Society (2020)
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13
Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks
In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 125-140 (2020) (2020)
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14
Jabberwocky Parsing: Dependency Parsing with Lexical Noise ...
Kasai, Jungo; Frank, Robert. - : University of Massachusetts Amherst, 2019
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15
Open Sesame: Getting Inside BERT's Linguistic Knowledge ...
Abstract: How and to what extent does BERT encode syntactically-sensitive hierarchical information or positionally-sensitive linear information? Recent work has shown that contextual representations like BERT perform well on tasks that require sensitivity to linguistic structure. We present here two studies which aim to provide a better understanding of the nature of BERT's representations. The first of these focuses on the identification of structurally-defined elements using diagnostic classifiers, while the second explores BERT's representation of subject-verb agreement and anaphor-antecedent dependencies through a quantitative assessment of self-attention vectors. In both cases, we find that BERT encodes positional information about word tokens well on its lower layers, but switches to a hierarchically-oriented encoding on higher layers. We conclude then that BERT's representations do indeed model linguistically relevant aspects of hierarchical structure, though they do not appear to show the sharp sensitivity to ... : To appear in the Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1906.01698
https://dx.doi.org/10.48550/arxiv.1906.01698
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16
Finding Syntactic Representations in Neural Stacks ...
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17
A Unified Analysis of Reflexives and Reciprocals in Synchronous Tree Adjoining Grammar
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18
Jabberwocky Parsing: Dependency Parsing with Lexical Noise
In: Proceedings of the Society for Computation in Linguistics (2019)
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19
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks ...
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20
Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages ...
McCoy, Richard T.; Frank, Robert. - : University of Massachusetts Amherst, 2018
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