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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 4 5 6...9 Hits 21 – 40 of 163 21 A Bayesian Framework for Information-Theoretic Probing ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Pimentel, Tiago. - : Underline Science Inc., 2021 BASE Show details 22 Classifying Dyads for Militarized Conflict Analysis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ahbab, Samin; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 23 Higher-order Derivatives of Weighted Finite-state Machines ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cotterell, Ryan; Vieira, Tim. - : Underline Science Inc., 2021 BASE Show details 24 On Finding the K-best Non-projective Dependency Trees ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cotterell, Ryan; Vieira, Tim. - : Underline Science Inc., 2021 BASE Show details 25 A surprisal--duration trade-off across and within the world's languages ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 26 Determinantal Beam Search ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Cotterell, Ryan; Forster, Martina. - : Underline Science Inc., 2021 BASE Show details 27 Is Sparse Attention more Interpretable? ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Augenstein, Isabelle; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 28 Revisiting the Uniform Information Density Hypothesis ... Meister, Clara Isabel; Pimentel, Tiago; Haller, Patrick. - : ETH Zurich, 2021 BASE Show details 29 A Plug-and-Play Method for Controlled Text Generation ... Pascual, Damian; Egressy, Béni; Meister, Clara Isabel. - : ETH Zurich, 2021 BASE Show details 30 Language Model Evaluation Beyond Perplexity ... Meister, Clara Isabel; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 31 What About the Precedent: An Information-Theoretic Analysis of Common Law ... Valvoda, Josef; Pimentel, Tiago; Stoehr, Niklas. - : ETH Zurich, 2021 BASE Show details 32 Searching for More Efficient Dynamic Programs ... Vieira, Tim; Cotterell, Ryan; Eisner, Jason. - : ETH Zurich, 2021 BASE Show details 33 Modeling the Unigram Distribution ... Nikkarinen, Irene; Pimentel, Tiago; Blasi, Damián. - : ETH Zurich, 2021 BASE Show details 34 Determinantal Beam Search ... Meister, Clara Isabel; Forster, Martina; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 35 Examining the Inductive Bias of Neural Language Models with Artificial Languages ... White, Jennifer C.; Cotterell, Ryan. - : ETH Zurich, 2021 Abstract: Since language models are used to model a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Investigation of these biases has proved complicated due to the many variables that appear in the experimental setup. Languages vary in many typological dimensions, and it is difficult to single out one or two to investigate without the others acting as confounders. We propose a novel method for investigating the inductive biases of language models using artificial languages. These languages are constructed to allow us to create parallel corpora across languages that differ only in the typological feature being investigated, such as word order. We then use them to train and test language models. This constitutes a fully controlled causal framework, and demonstrates how grammar engineering can serve as a useful tool for analyzing neural models. Using this method, we find that commonly used neural ... : Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing ... URL: https://dx.doi.org/10.3929/ethz-b-000519004 http://hdl.handle.net/20.500.11850/521265 BASE Hide details 36 Finding Concept-specific Biases in Form–Meaning Associations ... Pimentel, Tiago; Roark, Brian; Wichmann, Søren. - : ETH Zurich, 2021 BASE Show details 37 Differentiable subset pruning of transformer heads ... Li, Jiaoda; Cotterell, Ryan; Sachan, Mrinmaya. - : ETH Zurich, 2021 BASE Show details 38 On Finding the K-best Non-projective Dependency Trees ... Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 39 Efficient computation of expectations under spanning tree distributions ... Zmigrod, Ran; Vieira, Tim; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 40 Multimodal pretraining unmasked: A meta-analysis and a unified framework of vision-and-language berts ... Bugliarello, Emanuele; Cotterell, Ryan; Okazaki, Naoaki. - : ETH Zurich, 2021 BASE Show details Page: 1 2 3 4 5 6...9

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