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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 Page: 1 2 3 Hits 1 – 20 of 58 1 How Efficiency Shapes Human Language Gibson, Edward; Futrell, Richard; Piantadosi, Steven... In: https://hal.archives-ouvertes.fr/hal-03552539 ; 2022 (2022) BASE Show details 2 A verb-frame frequency account of constraints on long-distance dependencies in English Liu, Yingtong; Ryskin, Rachel; Futrell, Richard... In: Prof. Gibson (2022) BASE Show details 3 Dependency locality as an explanatory principle for word order Futrell, Richard; Levy, Roger P; Gibson, Edward A In: Prof. Levy (2022) BASE Show details 4 When classifying grammatical role, BERT doesn't care about word order... except when it matters ... Papadimitriou, Isabel; Futrell, Richard; Mahowald, Kyle. - : arXiv, 2022 BASE Show details 5 Grammatical cues are largely, but not completely, redundant with word meanings in natural language ... Mahowald, Kyle; Diachek, Evgeniia; Gibson, Edward. - : arXiv, 2022 BASE Show details 6 Learning Constraints on Wh-Dependencies by Learning How to Efficiently Represent Wh-Dependencies: A Developmental Modeling Investigation With Fragment Grammars Dickson, Niels; Pearl, Lisa; Futrell, Richard In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 7 When Classifying Arguments, BERT Doesn't Care About Word Order. Except When It Matters Papadimitriou, Isabel; Futrell, Richard; Mahowald, Kyle In: Proceedings of the Society for Computation in Linguistics (2022) BASE Show details 8 Word order affects the frequency of adjective use across languages Kachakeche, Zeinab; Futrell, Richard; Scontras, Gregory In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 9 Syntactic dependencies correspond to word pairs with high mutual information Futrell, Richard; Qian, Peng; Gibson, Edward A... In: Association for Computational Linguistics (2021) BASE Show details 10 Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations Wilcox, Ethan; Levy, Roger P; Futrell, Richard In: Association for Computational Linguistics (2021) BASE Show details 11 Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models Wilcox, Ethan; Futrell, Richard; Qian, Peng... In: Association for Computational Linguistics (2021) BASE Show details 12 Syntactic dependencies correspond to word pairs with high mutual information Futrell, Richard; Qian, Peng; Gibson, Edward... In: Association for Computational Linguistics (2021) BASE Show details 13 Structural Supervision Improves Learning of Non-Local Grammatical Dependencies Wilcox, Ethan; Qian, Peng; Futrell, Richard; Ballesteros, Miguel; Levy, Roger P In: Association for Computational Linguistics (2021) Abstract: © 2019 Association for Computational Linguistics State-of-the-art LSTM language models trained on large corpora learn sequential contingencies in impressive detail and have been shown to acquire a number of non-local grammatical dependencies with some success. Here we investigate whether supervision with hierarchical structure enhances learning of a range of grammatical dependencies, a question that has previously been addressed only for subject-verb agreement. Using controlled experimental methods from psycholinguistics, we compare the performance of word-based LSTM models versus two models that represent hierarchical structure and deploy it in left-to-right processing: Recurrent Neural Network Grammars (RNNGs) (Dyer et al., 2016) and a incrementalized version of the Parsing-as-Language-Modeling configuration from Charniak et al. (2016). Models are tested on a diverse range of configurations for two classes of non-local grammatical dependencies in English-Negative Polarity licensing and Filler-Gap Dependencies. Using the same training data across models, we find that structurally-supervised models outperform the LSTM, with the RNNG demonstrating best results on both types of grammatical dependencies and even learning many of the Island Constraints on the filler-gap dependency. Structural supervision thus provides data efficiency advantages over purely string-based training of neural language models in acquiring human-like generalizations about non-local grammatical dependencies. URL: https://hdl.handle.net/1721.1/137340.2 BASE Hide details 14 Maze Made Easy: Better and easier measurement of incremental processing difficulty Boyce, Veronica; Futrell, Richard; Levy, Roger P In: Other repository (2021) BASE Show details 15 An Information-Theoretic Characterization of Morphological Fusion ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Futrell, Richard; Hahn, Michael. - : Underline Science Inc., 2021 BASE Show details 16 Deep Subjecthood: Higher-Order Grammatical Features in Multilingual BERT ... Papadimitriou, Isabel; Chi, Ethan A.; Futrell, Richard. - : arXiv, 2021 BASE Show details 17 Multilingual BERT, Ergativity, and Grammatical Subjecthood ... Papadimitriou, Isabel; Chi, Ethan A.; Futrell, Richard. - : University of Massachusetts Amherst, 2021 BASE Show details 18 Sensitivity as a Complexity Measure for Sequence Classification Tasks ... Hahn, Michael; Jurafsky, Dan; Futrell, Richard. - : arXiv, 2021 BASE Show details 19 What do RNN Language Models Learn about Filler–Gap Dependencies? Wilcox, Ethan; Levy, Roger; Morita, Takashi... In: Association for Computational Linguistics (2021) BASE Show details 20 Language Learning and Processing in People and Machines Nematzadeh, Aida; Futrell, Richard; Levy, Roger P In: Association for Computational Linguistics (2021) BASE Show details Page: 1 2 3

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