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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...9 Hits 1 – 20 of 163 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 Isabel; Teufel, Simone. - : ETH Zurich, 2021 BASE Show details 5 On Homophony and Rényi Entropy ... Pimentel, Tiago; Meister, Clara; Teufel, Simone. - : arXiv, 2021 BASE Show details 6 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 7 Towards Zero-shot Language Modeling ... Ponti, Edoardo Maria; Vulić, Ivan; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 8 Differentiable Generative Phonology ... Wu, Shijie; Ponti, Edoardo Maria; Cotterell, Ryan. - : arXiv, 2021 BASE Show details 9 Finding Concept-specific Biases in Form--Meaning Associations ... Pimentel, Tiago; Roark, Brian; Wichmann, Søren. - : arXiv, 2021 BASE Show details 10 Searching for Search Errors in Neural Morphological Inflection ... Forster, Martina; Meister, Clara Isabel; Cotterell, Ryan. - : ETH Zurich, 2021 BASE Show details 11 Applying the Transformer to Character-level Transduction ... Wu, Shijie; Cotterell, Ryan; Hulden, Mans. - : ETH Zurich, 2021 BASE Show details 12 Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ... Stańczak, Karolina; Choudhury, Sagnik Ray; Pimentel, Tiago. - : arXiv, 2021 BASE Show details 13 Probing as Quantifying Inductive Bias ... Immer, Alexander; Hennigen, Lucas Torroba; Fortuin, Vincent. - : arXiv, 2021 BASE Show details 14 Revisiting the Uniform Information Density Hypothesis ... Meister, Clara; Pimentel, Tiago; Haller, Patrick. - : arXiv, 2021 BASE Show details 15 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 16 Conditional Poisson Stochastic Beams ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Amini, Afra; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 17 Examining the Inductive Bias of Neural Language Models with Artificial Languages ... 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; White, Jennifer. - : Underline Science Inc., 2021 BASE Show details 18 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 19 Language Model Evaluation Beyond Perplexity ... 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; Meister, Clara. - : Underline Science Inc., 2021 Abstract: Read paper: https://www.aclanthology.org/2021.acl-long.414 Abstract: We propose an alternate approach to quantifying how well language models learn natural language: we ask how well they match the statistical tendencies of natural language. To answer this question, we analyze whether text generated from language models exhibits the statistical tendencies present in the human-generated text on which they were trained. We provide a framework--paired with significance tests--for evaluating the fit of language models to these trends. We find that neural language models appear to learn only a subset of the tendencies considered, but align much more closely with empirical trends than proposed theoretical distributions (when present). Further, the fit to different distributions is highly-dependent on both model architecture and generation strategy. As concrete examples, text generated under the nucleus sampling scheme adheres more closely to the type--token relationship of natural language than text produced using ... Keyword: Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics URL: https://underline.io/lecture/25838-language-model-evaluation-beyond-perplexity https://dx.doi.org/10.48448/jr48-6p89 BASE Hide details 20 Differentiable Subset Pruning of Transformer Heads ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cotterell, Ryan; Li, Jiaoda. - : Underline Science Inc., 2021 BASE Show details Page: 1 2 3 4 5...9

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