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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 Hits 1 – 11 of 11 1 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 2 Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis ... Lepori, Michael A.; McCoy, R. Thomas. - : arXiv, 2020 BASE Show details 3 Universal linguistic inductive biases via meta-learning ... McCoy, R. Thomas; Grant, Erin; Smolensky, Paul. - : arXiv, 2020 BASE Show details 4 Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs ... Lepori, Michael A.; Linzen, Tal; McCoy, R. Thomas. - : arXiv, 2020 BASE Show details 5 Does Syntax Need to Grow on Trees? Sources of Hierarchical Inductive Bias in Sequence-to-Sequence Networks McCoy, R. Thomas; Frank, Robert; Linzen, Tal In: Transactions of the Association for Computational Linguistics, Vol 8, Pp 125-140 (2020) (2020) BASE Show details 6 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 7 What do you learn from context? Probing for sentence structure in contextualized word representations ... Tenney, Ian; Xia, Patrick; Chen, Berlin. - : arXiv, 2019 BASE Show details 8 Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference ... McCoy, R. Thomas; Pavlick, Ellie; Linzen, Tal. - : arXiv, 2019 BASE Show details 9 BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance ... McCoy, R. Thomas; Min, Junghyun; Linzen, Tal. - : arXiv, 2019 BASE Show details 10 Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks ... McCoy, R. Thomas; Frank, Robert; Linzen, Tal. - : arXiv, 2018 Abstract: Syntactic rules in natural language typically need to make reference to hierarchical sentence structure. However, the simple examples that language learners receive are often equally compatible with linear rules. Children consistently ignore these linear explanations and settle instead on the correct hierarchical one. This fact has motivated the proposal that the learner's hypothesis space is constrained to include only hierarchical rules. We examine this proposal using recurrent neural networks (RNNs), which are not constrained in such a way. We simulate the acquisition of question formation, a hierarchical transformation, in a fragment of English. We find that some RNN architectures tend to learn the hierarchical rule, suggesting that hierarchical cues within the language, combined with the implicit architectural biases inherent in certain RNNs, may be sufficient to induce hierarchical generalizations. The likelihood of acquiring the hierarchical generalization increased when the language included an ... : Proceedings of the 40th Annual Conference of the Cognitive Science Society; 10 pages ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1802.09091 https://dx.doi.org/10.48550/arxiv.1802.09091 BASE Hide details 11 TAG Parsing with Neural Networks and Vector Representations of Supertags Kasai, Jungo; Frank, Robert; Mccoy, R. Thomas... In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, ; Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01771494 ; Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhague, Denmark. pp.1712 - 1722 (2017) BASE Show details

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