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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 50 1 Probing for the Usage of Grammatical Number ... Lasri, Karim; Pimentel, Tiago; Lenci, Alessandro. - : arXiv, 2022 BASE Show details 2 Estimating the Entropy of Linguistic Distributions ... Arora, Aryaman; Meister, Clara; Cotterell, Ryan. - : arXiv, 2022 BASE Show details 3 A Latent-Variable Model for Intrinsic Probing ... Stańczak, Karolina; Hennigen, Lucas Torroba; Williams, Adina. - : arXiv, 2022 BASE Show details 4 On Homophony and Rényi Entropy ... Pimentel, Tiago; Meister, Clara; Teufel, Simone. - : arXiv, 2021 BASE Show details 5 Towards Zero-shot Language Modeling ... Ponti, Edoardo Maria; Vulić, Ivan; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 6 Differentiable Generative Phonology ... Wu, Shijie; Ponti, Edoardo Maria; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 7 Finding Concept-specific Biases in Form--Meaning Associations ... Pimentel, Tiago; Roark, Brian; Wichmann, Søren. - : arXiv, 2021 BASE Show details 8 Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ... Stańczak, Karolina; Choudhury, Sagnik Ray; Pimentel, Tiago. - : arXiv, 2021 BASE Show details 9 Probing as Quantifying Inductive Bias ... Immer, Alexander; Hennigen, Lucas Torroba; Fortuin, Vincent. - : arXiv, 2021 BASE Show details 10 Revisiting the Uniform Information Density Hypothesis ... Meister, Clara; Pimentel, Tiago; Haller, Patrick. - : arXiv, 2021 BASE Show details 11 How (Non-)Optimal is the Lexicon? ... Pimentel, Tiago; Nikkarinen, Irene; Mahowald, Kyle. - : arXiv, 2021 BASE Show details 12 Disambiguatory Signals are Stronger in Word-initial Positions ... Pimentel, Tiago; Cotterell, Ryan; Roark, Brian. - : arXiv, 2021 BASE Show details 13 A Cognitive Regularizer for Language Modeling ... Wei, Jason; Meister, Clara; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 14 Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing ... Maudslay, Rowan Hall; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 15 On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ... Williams, Adina; Cotterell, Ryan; Wolf-Sonkin, Lawrence. - : arXiv, 2020 BASE Show details 16 Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ... Leung, Jun Yen; Emerson, Guy; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 17 SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ... Vylomova, Ekaterina; White, Jennifer; Salesky, Elizabeth. - : arXiv, 2020 BASE Show details 18 Intrinsic Probing through Dimension Selection ... Hennigen, Lucas Torroba; Williams, Adina; Cotterell, Ryan. - : arXiv, 2020 BASE Show details 19 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 20 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 Page: 1 2 3

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