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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 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; Gao, Jianfeng; Celikyilmaz, Asli. - : arXiv, 2021 Abstract: Current language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apart these possibilities, we introduce RAVEN, a suite of analyses for assessing the novelty of generated text, focusing on sequential structure (n-grams) and syntactic structure. We apply these analyses to four neural language models (an LSTM, a Transformer, Transformer-XL, and GPT-2). For local structure - e.g., individual dependencies - model-generated text is substantially less novel than our baseline of human-generated text from each model's test set. For larger-scale structure - e.g., overall sentence structure - model-generated text is as novel or even more novel than the human-generated baseline, but models still sometimes copy substantially, in some cases duplicating passages over 1,000 words long from the training set. We also perform extensive manual analysis showing that GPT-2's novel text is usually well-formed ... : 10 pages, plus 39 pages of appendices ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2111.09509 https://dx.doi.org/10.48550/arxiv.2111.09509 BASE Hide 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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