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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) Abstract: Learners that are exposed to the same training data might generalize differently due to differing inductive biases. In neural network models, inductive biases could in theory arise from any aspect of the model architecture. We investigate which architectural factors affect the generalization behavior of neural sequence-to-sequence models trained on two syntactic tasks, English question formation and English tense reinflection. For both tasks, the training set is consistent with a generalization based on hierarchical structure and a generalization based on linear order. All architectural factors that we investigated qualitatively affected how models generalized, including factors with no clear connection to hierarchical structure. For example, LSTMs and GRUs displayed qualitatively different inductive biases. However, the only factor that consistently contributed a hierarchical bias across tasks was the use of a tree-structured model rather than a model with sequential recurrence, suggesting that human-like syntactic generalization requires architectural syntactic structure. Keyword: Computational linguistics. Natural language processing; P98-98.5 URL: https://doaj.org/article/ca442dfb7bd44ccf991dc7158480ae51 https://doi.org/10.1162/tacl_a_00304 BASE Hide 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 BASE Show 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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