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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 Hits 1 – 20 of 28 1 On Homophony and Rényi Entropy ... Pimentel, Tiago; Meister, Clara Isabel; Teufel, Simone. - : ETH Zurich, 2021 BASE Show details 2 On Homophony and Rényi Entropy ... Pimentel, Tiago; Meister, Clara; Teufel, Simone. - : arXiv, 2021 BASE Show details 3 On Homophony and Rényi Entropy ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Meister, Clara. - : Underline Science Inc., 2021 BASE Show details 4 Finding Concept-specific Biases in Form--Meaning Associations ... Pimentel, Tiago; Roark, Brian; Wichmann, Søren. - : arXiv, 2021 BASE Show details 5 Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ... Stańczak, Karolina; Choudhury, Sagnik Ray; Pimentel, Tiago. - : arXiv, 2021 BASE Show details 6 Revisiting the Uniform Information Density Hypothesis ... Meister, Clara; Pimentel, Tiago; Haller, Patrick. - : arXiv, 2021 BASE Show details 7 Revisiting the Uniform Information Density Hypothesis ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Haller, Patrick. - : Underline Science Inc., 2021 BASE Show details 8 Modeling the Unigram Distribution ... 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; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 9 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 10 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 11 Revisiting the Uniform Information Density Hypothesis ... Meister, Clara Isabel; Pimentel, Tiago; Haller, Patrick. - : ETH Zurich, 2021 BASE Show details 12 What About the Precedent: An Information-Theoretic Analysis of Common Law ... Valvoda, Josef; Pimentel, Tiago; Stoehr, Niklas. - : ETH Zurich, 2021 BASE Show details 13 Modeling the Unigram Distribution ... Nikkarinen, Irene; Pimentel, Tiago; Blasi, Damián. - : ETH Zurich, 2021 BASE Show details 14 Finding Concept-specific Biases in Form–Meaning Associations ... Pimentel, Tiago; Roark, Brian; Wichmann, Søren. - : ETH Zurich, 2021 BASE Show details 15 How (Non-)Optimal is the Lexicon? ... Pimentel, Tiago; Nikkarinen, Irene; Mahowald, Kyle. - : arXiv, 2021 BASE Show details 16 Disambiguatory Signals are Stronger in Word-initial Positions ... Pimentel, Tiago; Cotterell, Ryan; Roark, Brian. - : arXiv, 2021 BASE Show details 17 Modeling the Unigram Distribution Blasi, Damián; Pimentel, Tiago; Nikkarinen, Irene... In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021) BASE Show details 18 What About the Precedent: An Information-Theoretic Analysis of Common Law Teufel, Simone; Valvoda, Josef; Stoehr, Niklas... In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021) BASE Show details 19 Finding Concept-specific Biases in Form–Meaning Associations Roark, Brian; Blasi, Damián; Pimentel, Tiago... In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021) BASE Show details 20 A Non-Linear Structural Probe Pimentel, Tiago; Cotterell, Ryan; Saphra, Naomi; White, Jennifer C. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021) Abstract: Probes are models devised to investigate the encoding of knowledge—e.g. syntactic structure—in contextual representations. Probes are often designed for simplicity, which has led to restrictions on probe design that may not allow for the full exploitation of the structure of encoded information; one such restriction is linearity. We examine the case of a structural probe (Hewitt and Manning, 2019), which aims to investigate the encoding of syntactic structure in contextual representations through learning only linear transformations. By observing that the structural probe learns a metric, we are able to kernelize it and develop a novel non-linear variant with an identical number of parameters. We test on 6 languages and find that the radial-basis function (RBF) kernel, in conjunction with regularization, achieves a statistically significant improvement over the baseline in all languages—implying that at least part of the syntactic knowledge is encoded non-linearly. We conclude by discussing how the RBF kernel resembles BERT’s self-attention layers and speculate that this resemblance leads to the RBF-based probe’s stronger performance. URL: https://hdl.handle.net/20.500.11850/518983 https://doi.org/10.3929/ethz-b-000518983 BASE Hide details Page: 1 2

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