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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. - : Underline Science Inc., 2021 BASE Show 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 Abstract: Temporary syntactic ambiguities arise when the beginning of a sentence is compatible with multiple syntactic analyses. We inspect to which extent neural language models (LMs) exhibit uncertainty over such analyses when processing temporarily ambiguous inputs, and how that uncertainty is modulated by disambiguating cues. We probe the LM's expectations by generating from it: we use stochastic decoding to derive a set of sentence completions, and estimate the probability that the LM assigns to each interpretation based on the distribution of parses across completions. Unlike scoring-based methods for targeted syntactic evaluation, this technique makes it possible to explore completions that are not hypothesized in advance by the researcher. We apply this method to study the behavior of two LMs (GPT2 and an LSTM) on three types of temporary ambiguity, using materials from human sentence processing experiments. We find that LMs can track multiple analyses simultaneously; the degree of uncertainty varies across ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network URL: https://underline.io/lecture/39899-the-language-model-understood-the-prompt-was-ambiguous-probing-syntactic-uncertainty-through-generation https://dx.doi.org/10.48448/4xft-c381 BASE Hide 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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