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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 86 1 Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit Complexity ... Hao, Yiding; Angluin, Dana; Frank, Robert. - : arXiv, 2022 BASE Show details 2 Do Language Models Learn Position-Role Mappings? ... Petty, Jackson; Wilson, Michael; Frank, Robert. - : arXiv, 2022 BASE Show details 3 Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models ... Mueller, Aaron; Frank, Robert; Linzen, Tal. - : arXiv, 2022 BASE Show details 4 Arguments for top-down derivations in syntax Frank, Robert; Kotek, Hadas In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5264 ; 2473-8689 (2022) BASE Show details 5 Structure Here, Bias There: Hierarchical Generalization by Jointly Learning Syntactic Transformations Mulligan, Karl; Frank, Robert; Linzen, Tal In: Proceedings of the Society for Computation in Linguistics (2021) Abstract: When learning syntactic transformations, children consistently induce structure-dependent generalizations, even though the primary linguistic data may be consistent with both linear and hierarchical rules. What is the source of this inductive bias? In this paper, we use computational models to investigate the hypothesis that evidence for the structure-sensitivity of one syntactic transformation can bias the acquisition of another transformation in favor of a hierarchical rule. We train sequence-to-sequence models based on artificial neural networks to learn multiple syntactic transformations at the same time in a fragment of English; we hold out cases that disambiguate linear and hierarchical rules for one of those transformations, and then test for hierarchical generalization to these held-out sentence types. Consistent with our hypothesis, we find that multitask learning induces a hierarchical bias for certain combinations of tasks, and that this bias is stronger for transformations that share computational building blocks. At the same time, the bias is in general insufficient to lead the learner to categorically acquire the hierarchical generalization for the target transformation. Keyword: Computational Linguistics; inductive bias; multitask learning; poverty of the stimulus; structure dependence URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1221&context=scil https://scholarworks.umass.edu/scil/vol4/iss1/13 BASE Hide details 6 Comparing methods of tree-construction across mildly context-sensitive formalisms Hunter, Tim; Frank, Robert In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 7 The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions ... Srivastava, Aarohi; Frank, Robert; Widder, Sarah. - : University of Mass Amherst, 2020 BASE Show details 8 Sequence-to-Sequence Networks Learn the Meaning of Reflexive Anaphora ... Frank, Robert; Petty, Jackson. - : arXiv, 2020 BASE Show details 9 Sequence-to-Sequence Networks Learn the Meaning of Reflexive Anaphora ... The 28th International Conference on Computational Linguistics 2020; Frank, Robert; Petty, Jackson. - : Underline Science Inc., 2020 BASE Show details 10 Probabilistic Predictions of People Perusing: Evaluating Metrics of Language Model Performance for Psycholinguistic Modeling ... Hao, Yiding; Mendelsohn, Simon; Sterneck, Rachel. - : arXiv, 2020 BASE Show details 11 The Role of Linguistic Features in Domain Adaptation: TAG Parsing of Questions Srivastava, Aarohi; Frank, Robert; Widder, Sarah... In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 12 Primitive Asymmetric C-Command Derives X̄-Theory Frank, Robert; Kuminiak, Fero In: North East Linguistics Society (2020) BASE Show details 13 Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks McCoy, R. Thomas; Frank, Robert; Linzen, Tal In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 125-140 (2020) (2020) BASE Show details 14 Jabberwocky Parsing: Dependency Parsing with Lexical Noise ... Kasai, Jungo; Frank, Robert. - : University of Massachusetts Amherst, 2019 BASE Show details 15 Open Sesame: Getting Inside BERT's Linguistic Knowledge ... Lin, Yongjie; Tan, Yi Chern; Frank, Robert. - : arXiv, 2019 BASE Show details 16 Finding Syntactic Representations in Neural Stacks ... Merrill, William; Khazan, Lenny; Amsel, Noah. - : arXiv, 2019 BASE Show details 17 A Unified Analysis of Reflexives and Reciprocals in Synchronous Tree Adjoining Grammar Aggazzotti, Cristina. - 2019 BASE Show details 18 Jabberwocky Parsing: Dependency Parsing with Lexical Noise Kasai, Jungo; Frank, Robert In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 19 Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks ... McCoy, R. Thomas; Frank, Robert; Linzen, Tal. - : arXiv, 2018 BASE Show details 20 Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages ... McCoy, Richard T.; Frank, Robert. - : University of Massachusetts Amherst, 2018 BASE Show details Page: 1 2 3 4 5

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