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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 6 Hits 1 – 20 of 112 1 Optimality Theory: Constraint Interaction in Generative Grammar ... Smolensky, Paul; Prince, Alan S.. - : Rutgers University, 2022 BASE Show details 2 Compositional processing emerges in neural networks solving math problems Russin, Jacob; Fernandez, Roland; Palangi, Hamid... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 3 Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar McCoy, Richard Thomas; Culbertson, Jennifer; Smolensky, Paul... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 4 Compositional Processing Emerges in Neural Networks Solving Math Problems ... Russin, Jacob; Fernandez, Roland; Palangi, Hamid. - : arXiv, 2021 BASE Show details 5 Distributed neural encoding of binding to thematic roles ... Lalisse, Matthias; Smolensky, Paul. - : arXiv, 2021 BASE Show details 6 Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar ... 43rd Annual Meeting of the Cognitive Science Society 2021; Culbertson, Jennifer; Legendre, Geraldine. - : Underline Science Inc., 2021 BASE Show details 7 Compositional processing emerges in neural networks solving math problems ... 43rd Annual Meeting of the Cognitive Science Society 2021; Fernandez, Roland; Gao, Jianfeng. - : 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 Compositional Processing Emerges in Neural Networks Solving Math Problems Russin, Jacob; Fernandez, Roland; Palangi, Hamid... In: Cogsci (2021) BASE Show details 10 Emergent Gestural Scores in a Recurrent Neural Network Model of Vowel Harmony Smolensky, Paul; Smith, Caitlin; Rosen, Eric... In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 11 Testing for Grammatical Category Abstraction in Neural Language Models Kim, Najoung; Smolensky, Paul In: Proceedings of the Society for Computation in Linguistics (2021) BASE Show details 12 Universal linguistic inductive biases via meta-learning ... McCoy, R. Thomas; Grant, Erin; Smolensky, Paul. - : arXiv, 2020 BASE Show details 13 Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks McCoy, Richard T; Linzen, Tal; Dunbar, Ewan... In: Proceedings of the Society for Computation in Linguistics (2020) BASE Show details 14 Learning a gradient grammar of French liaison Smolensky, Paul; Rosen, Eric; Goldrick, Matthew In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2019 Annual Meeting on Phonology ; 2377-3324 (2020) BASE Show details 15 RNNs Implicitly Implement Tensor Product Representations Mccoy, R. Thomas; Linzen, Tal; Dunbar, Ewan... In: International Conference on Learning Representations ; ICLR 2019 - International Conference on Learning Representations ; https://hal.archives-ouvertes.fr/hal-02274498 ; ICLR 2019 - International Conference on Learning Representations, May 2019, New Orleans, United States (2019) BASE Show details 16 Quantum Language Processing ... Wiebe, Nathan; Bocharov, Alex; Smolensky, Paul. - : arXiv, 2019 BASE Show details 17 Transient blend states and discrete agreement-driven errors in sentence production Goldrick, Matthew; Brehm, Laurel; Cho, Pyeong Whan... In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 18 Augmentic Compositional Models for Knowledge Base Completion Using Gradient Representations Lalisse, Matthias R; Smolensky, Paul In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details 19 Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations ... Lalisse, Matthias; Smolensky, Paul. - : arXiv, 2018 BASE Show details 20 A Simple Recurrent Unit with Reduced Tensor Product Representations ... Tang, Shuai; Smolensky, Paul; de Sa, Virginia R.. - : arXiv, 2018 BASE Show details Page: 1 2 3 4 5 6

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