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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 BASE Show details 11 TAG Parsing with Neural Networks and Vector Representations of Supertags Kasai, Jungo; Frank, Robert; Mccoy, R. Thomas; Rambow, Owen; Nasr, Alexis 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) Abstract: International audience ; We present supertagging-based models for Tree Adjoining Grammar parsing that use neural network architectures and dense vector representation of supertags (elementary trees) to achieve state-of-the-art performance in unlabeled and labeled attachment scores. The shift-reduce parsing model eschews lexical information entirely , and uses only the 1-best supertags to parse a sentence, providing further support for the claim that supertagging is " almost parsing. " We demonstrate that the embedding vector representations the parser induces for supertags possess linguistically interpretable structure, supporting analogies between grammatical structures like those familiar from recent work in distri-butional semantics. This dense representation of supertags overcomes the drawbacks for statistical models of TAG as compared to CCG parsing, raising the possibility that TAG is a viable alternative for NLP tasks that require the assignment of richer structural descriptions to sentences. Keyword: [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing URL: https://hal.archives-ouvertes.fr/hal-01771494/file/D17-1180.pdf https://hal.archives-ouvertes.fr/hal-01771494/document https://hal.archives-ouvertes.fr/hal-01771494 BASE Hide details

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