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Optimality Theory: Constraint Interaction in Generative Grammar ...
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Abstract:
This work develops a conception of grammar in which optimality with respect to a set of constraints defines well-formedness. The argument begins with a brief assessment of the promise of optimization-based approaches, focusing on issues of explanation from principle. The general lay-out of Optimality Theory is sketched, including the core notions of ranking & violability and the emphasis on universality in the constraint set.Part I shows how the ideas play out over a variety of phenomena and generalization patterns. The key distinction between Markedness and Faithfulness constraints is introduced. The analytical focus is on empirical phenomena ranging from epenthesis to infixation to a variety of sometimes-complex interactions between prominence, syllabification, stress, and word form. Part I concludes with a formal presentation of the theory.Part II investigates the theory of syllable structure. It begins with a study of the basic Jakobson typology and moves on to present an analysis of aspects of the ...
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Keyword:
Faithfulness; Formal Analysis; Markedness Linguistics; Optimality; Phonology; Ranking; Universal; Violable
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URL: https://dx.doi.org/10.7282/t34m92mv https://scholarship.libraries.rutgers.edu/esploro/outputs/technicalDocumentation/991031549929204646
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Compositional processing emerges in neural networks solving math problems
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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Compositional Processing Emerges in Neural Networks Solving Math Problems ...
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Distributed neural encoding of binding to thematic roles ...
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Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar ...
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Compositional processing emerges in neural networks solving math problems ...
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How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN ...
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Compositional Processing Emerges in Neural Networks Solving Math Problems
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In: Cogsci (2021)
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Emergent Gestural Scores in a Recurrent Neural Network Model of Vowel Harmony
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Testing for Grammatical Category Abstraction in Neural Language Models
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In: Proceedings of the Society for Computation in Linguistics (2021)
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Universal linguistic inductive biases via meta-learning ...
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Tensor Product Decomposition Networks: Uncovering Representations of Structure Learned by Neural Networks
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In: Proceedings of the Society for Computation in Linguistics (2020)
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Learning a gradient grammar of French liaison
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In: Proceedings of the Annual Meetings on Phonology; Proceedings of the 2019 Annual Meeting on Phonology ; 2377-3324 (2020)
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RNNs Implicitly Implement Tensor Product Representations
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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)
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Transient blend states and discrete agreement-driven errors in sentence production
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Augmentic Compositional Models for Knowledge Base Completion Using Gradient Representations
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In: Proceedings of the Society for Computation in Linguistics (2019)
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Augmenting Compositional Models for Knowledge Base Completion Using Gradient Representations ...
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A Simple Recurrent Unit with Reduced Tensor Product Representations ...
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