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Optimality Theory: Constraint Interaction in Generative Grammar ...
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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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Abstract:
How do learners acquire languages from the limited data available to them? This process must involve some inductive biases - factors that affect how a learner generalizes - but it is unclear which inductive biases can explain observed patterns in language acquisition. To facilitate computational modeling aimed at addressing this question, we introduce a framework for giving particular linguistic inductive biases to a neural network model; such a model can then be used to empirically explore the effects of those inductive biases. This framework disentangles universal inductive biases, which are encoded in the initial values of a neural network's parameters, from non-universal factors, which the neural network must learn from data in a given language. The initial state that encodes the inductive biases is found with meta-learning, a technique through which a model discovers how to acquire new languages more easily via exposure to many possible languages. By controlling the properties of the languages that are ... : To appear in the Proceedings of the 42nd Annual Conference of the Cognitive Science Society ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2006.16324 https://arxiv.org/abs/2006.16324
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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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