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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 – 12 of 12 1 Towards Interactive Language Modeling ... ter Hoeve, Maartje; Kharitonov, Evgeny; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 2 Language Models Use Monotonicity to Assess NPI Licensing ... Jumelet, Jaap; Denić, Milica; Szymanik, Jakub. - : arXiv, 2021 BASE Show details 3 Language Modelling as a Multi-Task Problem ... Weber, Lucas; Jumelet, Jaap; Bruni, Elia. - : arXiv, 2021 BASE Show details 4 How BPE Affects Memorization in Transformers ... Kharitonov, Eugene; Baroni, Marco; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 5 Causal Transformers Perform Below Chance on Recursive Nested Constructions, Unlike Humans ... Lakretz, Yair; Desbordes, Théo; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 6 Sparse Interventions in Language Models with Differentiable Masking ... De Cao, Nicola; Schmid, Leon; Hupkes, Dieuwke. - : arXiv, 2021 BASE Show details 7 Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans ... Lakretz, Yair; Hupkes, Dieuwke; Vergallito, Alessandra; Marelli, Marco; Baroni, Marco; Dehaene, Stanislas. - : arXiv, 2020 Abstract: Recursive processing in sentence comprehension is considered a hallmark of human linguistic abilities. However, its underlying neural mechanisms remain largely unknown. We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing, namely the storing of grammatical number and gender information in working memory and its use in long-distance agreement (e.g., capturing the correct number agreement between subject and verb when they are separated by other phrases). Although the network, a recurrent architecture with Long Short-Term Memory units, was solely trained to predict the next word in a large corpus, analysis showed the emergence of a very sparse set of specialized units that successfully handled local and long-distance syntactic agreement for grammatical number. However, the simulations also showed that this mechanism does not support full recursion and fails with some long-range embedded dependencies. We tested the ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2006.11098 https://dx.doi.org/10.48550/arxiv.2006.11098 BASE Hide details 8 Assessing incrementality in sequence-to-sequence models ... Ulmer, Dennis; Hupkes, Dieuwke; Bruni, Elia. - : arXiv, 2019 BASE Show details 9 Compositionality decomposed: how do neural networks generalise? ... Hupkes, Dieuwke; Dankers, Verna; Mul, Mathijs. - : arXiv, 2019 BASE Show details 10 Formal models of structure building in music, language, and animal song Zuidema, Willem; Hupkes, Dieuwke; Wiggins, Geraint A.... In: The origins of musicality (Cambridge, 2018), p. 253-286 MPI für Psycholinguistik Show details 11 Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information ... Giulianelli, Mario; Harding, Jacqueline; Mohnert, Florian. - : arXiv, 2018 BASE Show details 12 Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items ... Jumelet, Jaap; Hupkes, Dieuwke. - : arXiv, 2018 BASE Show details

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