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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) Abstract: We probe nouns in BERT contextual embedding space for grammatical role (subject vs. object of a clause), and examine how probing results vary between prototypical examples, where the role matches what we would expect from seeing that word in the context, and non-prototypical examples, where the role is mostly imparted by the context. In this way, engage with the contrast that has arisen in the literature, between studies that show contextual models as grammatically sensitive, and others that show that these models are robust to changes in word order. Our experiments yield three results: 1) Grammatical role is recovered in later layers for difficult non-prototypical cases, while prototypical cases are accurate without many layers of context 2) When we switch the subject and the object of a sentence around (eg, The chef cut the onion, The onion cut the chef), we see that the same word (eg, onion) can be fluently identified as both a subject and an object 3) Subjecthood probing breaks if we ablate local word order by shuffle words locally and break grammaticality. Keyword: BERT; Computational Linguistics; Contextual embeddings; grammatical role; prototype; subjecthood; verb arguments; word order URL: https://scholarworks.umass.edu/scil/vol5/iss1/18 https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1244&context=scil BASE Hide 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... In: Association for Computational Linguistics (2021) BASE Show 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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