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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 Hits 1 – 20 of 54 1 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 2 Does Putting a Linguist in the Loop Improve NLU Data Collection? ... Parrish, Alicia; Huang, William; Agha, Omar. - : arXiv, 2021 BASE Show details 3 NOPE: A Corpus of Naturally-Occurring Presuppositions in English ... Parrish, Alicia; Schuster, Sebastian; Warstadt, Alex. - : arXiv, 2021 BASE Show details 4 NOPE: A Corpus of Naturally-Occurring Presuppositions in English ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Alex; Agha, Omar. - : Underline Science Inc., 2021 BASE Show details 5 Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Belinkov, Yonatan; Finlayson, Matthew; Gehrmann, Sebastian; Linzen, Tal; Mueller, Aaron; Shieber, Stuart. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.144 Abstract: Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models. We investigate the magnitude of models' preferences for grammatical inflections, as well as whether neurons process subject-verb agreement similarly across sentences with different syntactic structures. We uncover similarities and differences across architectures and model sizes—notably, that larger models do not necessarily learn stronger preferences. We also observe two distinct mechanisms for producing subject-verb agreement depending on the syntactic structure of the input sentence. Finally, we find that language models rely on similar sets of neurons when given sentences with similar syntactic structure. ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://dx.doi.org/10.48448/76fq-n338 https://underline.io/lecture/25457-causal-analysis-of-syntactic-agreement-mechanisms-in-neural-language-models BASE Hide details 6 Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ... Ravfogel, Shauli; Prasad, Grusha; Linzen, Tal. - : arXiv, 2021 BASE Show details 7 The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Aina, Laura; Linzen, Tal. - : Underline Science Inc., 2021 BASE Show details 8 How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN ... McCoy, R. Thomas; Smolensky, Paul; Linzen, Tal. - : arXiv, 2021 BASE Show details 9 Frequency Effects on Syntactic Rule Learning in Transformers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Garrette, Dan; Linzen, Tal. - : Underline Science Inc., 2021 BASE Show details 10 Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Goldberg, Yoav; Linzen, Tal. - : Underline Science Inc., 2021 BASE Show details 11 Frequency Effects on Syntactic Rule Learning in Transformers ... Wei, Jason; Garrette, Dan; Linzen, Tal. - : arXiv, 2021 BASE Show details 12 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) BASE Show details 13 Priming syntactic ambiguity resolution in children and adults Havron, Naomi; Scaff, Camila; Carbajal, Maria Julia... In: ISSN: 2327-3798 ; EISSN: 2327-3801 ; Language, Cognition and Neuroscience ; https://hal.archives-ouvertes.fr/hal-03099573 ; Language, Cognition and Neuroscience, Taylor and Francis, 2020, 35 (10), pp.1445-1455. ⟨10.1080/23273798.2020.1797130⟩ (2020) BASE Show details 14 Priming syntactic ambiguity resolution in children and adults ... Havron, Naomi; Scaff, Camila; Carbajal, Maria Julia. - : Taylor & Francis, 2020 BASE Show details 15 Priming syntactic ambiguity resolution in children and adults ... Havron, Naomi; Scaff, Camila; Carbajal, Maria Julia. - : Taylor & Francis, 2020 BASE Show details 16 How Can We Accelerate Progress Towards Human-like Linguistic Generalization? ... Linzen, Tal. - : arXiv, 2020 BASE Show details 17 Universal linguistic inductive biases via meta-learning ... McCoy, R. Thomas; Grant, Erin; Smolensky, Paul. - : arXiv, 2020 BASE Show details 18 The reliability of acceptability judgments across languages. Glossa: a journal of general linguistics, 3(1), 100. ... Linzen, Tal. - : Zenodo, 2020 BASE Show details 19 The reliability of acceptability judgments across languages. Glossa: a journal of general linguistics, 3(1), 100. ... Linzen, Tal. - : Zenodo, 2020 BASE Show details 20 Invited Talk: Neural networks as cognitive models of syntax - Tal Linzen ... The 28th International Conference on Computational Linguistics 2020; Linzen, Tal. - : Underline Science Inc., 2020 BASE Show details Page: 1 2 3

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