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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 ...
Abstract: Reflexive anaphora present a challenge for semantic interpretation: their meaning varies depending on context in a way that appears to require abstract variables. Past work has raised doubts about the ability of recurrent networks to meet this challenge. In this paper, we explore this question in the context of a fragment of English that incorporates the relevant sort of contextual variability. We consider sequence-to-sequence architectures with recurrent units and show that such networks are capable of learning semantic interpretations for reflexive anaphora which generalize to novel antecedents. We explore the effect of attention mechanisms and different recurrent unit types on the type of training data that is needed for success as measured in two ways: how much lexical support is needed to induce an abstract reflexive meaning (i.e., how many distinct reflexive antecedents must occur during training) and what contexts must a noun phrase occur in to support generalization of reflexive interpretation to ...
Keyword: Computer and Information Science; Natural Language Processing; Neural Network
URL: https://dx.doi.org/10.48448/n02b-pg76
https://underline.io/lecture/6124-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 ...
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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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