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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 37 1 A Latent-Variable Model for Intrinsic Probing ... Stańczak, Karolina; Hennigen, Lucas Torroba; Williams, Adina. - : arXiv, 2022 BASE Show details 2 A Neighbourhood Framework for Resource-Lean Content Flagging ... Sarwar, Sheikh Muhammad; Zlatkova, Dimitrina; Hardalov, Momchil. - : arXiv, 2021 BASE Show details 3 A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives ... Rethmeier, Nils; Augenstein, Isabelle. - : arXiv, 2021 BASE Show details 4 QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension ... Rogers, Anna; Gardner, Matt; Augenstein, Isabelle. - : arXiv, 2021 BASE Show details 5 Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ... Stańczak, Karolina; Choudhury, Sagnik Ray; Pimentel, Tiago; Cotterell, Ryan; Augenstein, Isabelle. - : arXiv, 2021 Abstract: While the prevalence of large pre-trained language models has led to significant improvements in the performance of NLP systems, recent research has demonstrated that these models inherit societal biases extant in natural language. In this paper, we explore a simple method to probe pre-trained language models for gender bias, which we use to effect a multi-lingual study of gender bias towards politicians. We construct a dataset of 250k politicians from most countries in the world and quantify adjective and verb usage around those politicians' names as a function of their gender. We conduct our study in 7 languages across 6 different language modeling architectures. Our results demonstrate that stance towards politicians in pre-trained language models is highly dependent on the language used. Finally, contrary to previous findings, our study suggests that larger language models do not tend to be significantly more gender-biased than smaller ones. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning stat.ML URL: https://dx.doi.org/10.48550/arxiv.2104.07505 https://arxiv.org/abs/2104.07505 BASE Hide details 6 Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ... Hardalov, Momchil; Arora, Arnav; Nakov, Preslav. - : arXiv, 2021 BASE Show details 7 Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models? ... Choudhury, Sagnik Ray; Bhutani, Nikita; Augenstein, Isabelle. - : arXiv, 2021 BASE Show details 8 CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding ... 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; Augenstein, Isabelle; Wright, Dustin. - : Underline Science Inc., 2021 BASE Show details 9 How Does Counterfactually Augmented Data Impact Models for Social Computing Constructs? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Augenstein, Isabelle; Flöck, Fabian. - : Underline Science Inc., 2021 BASE Show details 10 Is Sparse Attention more Interpretable? ... 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; Augenstein, Isabelle; Cotterell, Ryan. - : Underline Science Inc., 2021 BASE Show details 11 Is Sparse Attention more Interpretable? ... Meister, Clara Isabel; Lazov, Stefan; Augenstein, Isabelle. - : ETH Zurich, 2021 BASE Show details 12 Quantifying Gender Biases Towards Politicians on Reddit ... Marjanovic, Sara; Stańczak, Karolina; Augenstein, Isabelle. - : arXiv, 2021 BASE Show details 13 A Survey on Gender Bias in Natural Language Processing ... Stanczak, Karolina; Augenstein, Isabelle. - : arXiv, 2021 BASE Show details 14 Semi-Supervised Exaggeration Detection of Health Science Press Releases ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Augenstein, Isabelle; Wright, Dustin. - : Underline Science Inc., 2021 BASE Show details 15 Inducing Language-Agnostic Multilingual Representations ... Zhao, Wei; Eger, Steffen; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 16 Zero-Shot Cross-Lingual Transfer with Meta Learning ... Nooralahzadeh, Farhad; Bekoulis, Giannis; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 17 SIGTYP 2020 Shared Task: Prediction of Typological Features ... Bjerva, Johannes; Salesky, Elizabeth; Mielke, Sabrina J.. - : arXiv, 2020 BASE Show details 18 Generating Fact Checking Explanations ... Atanasova, Pepa; Simonsen, Jakob Grue; Lioma, Christina. - : arXiv, 2020 BASE Show details 19 X-WikiRE: A Large, Multilingual Resource for Relation Extraction as Machine Comprehension ... Abdou, Mostafa; Sas, Cezar; Aralikatte, Rahul. - : arXiv, 2019 BASE Show details 20 TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP ... Rethmeier, Nils; Saxena, Vageesh Kumar; Augenstein, Isabelle. - : arXiv, 2019 BASE Show details Page: 1 2

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