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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 – 10 of 10 1 What About the Precedent: An Information-Theoretic Analysis of Common Law ... Valvoda, Josef; Pimentel, Tiago; Stoehr, Niklas. - : ETH Zurich, 2021 BASE Show details 2 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 3 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 4 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan Hall; Zmigrod, Ran; Williams, Adina; Cotterell, Ryan. - : arXiv, 2020 Abstract: The success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually ``know'' about natural language. Probes are a natural way of assessing this. When probing, a researcher chooses a linguistic task and trains a supervised model to predict annotations in that linguistic task from the network's learned representations. If the probe does well, the researcher may conclude that the representations encode knowledge related to the task. A commonly held belief is that using simpler models as probes is better; the logic is that simpler models will identify linguistic structure, but not learn the task itself. We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation. ... : Accepted for publication at ACL 2020. This is the camera ready version. Code available in https://github.com/rycolab/info-theoretic-probing ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2004.03061 https://arxiv.org/abs/2004.03061 BASE Hide details 5 Information-Theoretic Probing for Linguistic Structure ... Pimentel, Tiago; Valvoda, Josef; Maudslay, Rowan H.. - : ETH Zurich, 2020 BASE Show details 6 A Tale of a Probe and a Parser Valvoda, Josef; Hall Maudslay, Rowan; Williams, Adina... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 7 Information-Theoretic Probing for Linguistic Structure Pimentel, Tiago; Zmigrod, Ran; Cotterell, Ryan... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 8 A Tale of a Probe and a Parser ... Hall Maudslay, Rowan; Valvoda, Josef; Pimentel, Tiago. - : ETH Zurich, 2020 BASE Show details 9 A Tale of a Probe and a Parser ... Maudslay, Rowan Hall; Valvoda, Josef; Pimentel, Tiago. - : arXiv, 2020 BASE Show details 10 Analyzing Neural Discourse Coherence Models ... Farag, Youmna; Valvoda, Josef; Yannakoudakis, Helen. - : arXiv, 2020 BASE Show details

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