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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 – 8 of 8 1 XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation ... Ruder, Sebastian; Constant, Noah; Botha, Jan. - : arXiv, 2021 BASE Show details 2 MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network ... FitzGerald, Nicholas; Botha, Jan A.; Gillick, Daniel. - : arXiv, 2021 BASE Show details 3 MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network ... 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; Bikel, Daniel; Botha, Jan. - : Underline Science Inc., 2021 BASE Show details 4 Entity Linking in 100 Languages ... Botha, Jan A.; Shan, Zifei; Gillick, Daniel. - : arXiv, 2020 BASE Show details 5 Asking without Telling: Exploring Latent Ontologies in Contextual Representations ... Michael, Julian; Botha, Jan A.; Tenney, Ian. - : arXiv, 2020 Abstract: The success of pretrained contextual encoders, such as ELMo and BERT, has brought a great deal of interest in what these models learn: do they, without explicit supervision, learn to encode meaningful notions of linguistic structure? If so, how is this structure encoded? To investigate this, we introduce latent subclass learning (LSL): a modification to existing classifier-based probing methods that induces a latent categorization (or ontology) of the probe's inputs. Without access to fine-grained gold labels, LSL extracts emergent structure from input representations in an interpretable and quantifiable form. In experiments, we find strong evidence of familiar categories, such as a notion of personhood in ELMo, as well as novel ontological distinctions, such as a preference for fine-grained semantic roles on core arguments. Our results provide unique new evidence of emergent structure in pretrained encoders, including departures from existing annotations which are inaccessible to earlier methods. ... : 21 pages, 8 figures, 11 tables. Published in EMNLP 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7 URL: https://arxiv.org/abs/2004.14513 https://dx.doi.org/10.48550/arxiv.2004.14513 BASE Hide details 6 Cross-Lingual Morphological Tagging for Low-Resource Languages ... Buys, Jan; Botha, Jan A.. - : arXiv, 2016 BASE Show details 7 Bayesian Language Modelling of German Compounds ... Botha, Jan; Dyer, Chris; Blunsom, Phil. - : Carnegie Mellon University, 2012 BASE Show details 8 Bayesian Language Modelling of German Compounds ... Botha, Jan; Dyer, Chris; Blunsom, Phil. - : Carnegie Mellon University, 2012 BASE Show details

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