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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. - : arXiv, 2021 BASE Show 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 Abstract: While state-of-the-art NLP explainability (XAI) methods focus on explaining per-sample decisions in supervised end or probing tasks, this is insufficient to explain and quantify model knowledge transfer during (un-)supervised training. Thus, for TX-Ray, we modify the established computer vision explainability principle of 'visualizing preferred inputs of neurons' to make it usable transfer analysis and NLP. This allows one to analyze, track and quantify how self- or supervised NLP models first build knowledge abstractions in pretraining (1), and then transfer these abstractions to a new domain (2), or adapt them during supervised fine-tuning (3). TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. We find that, similar to Lottery Ticket based pruning, TX-Ray based pruning can improve test set generalization and that it can reveal how early stages of self-supervision automatically learn linguistic abstractions like ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML URL: https://arxiv.org/abs/1912.00982 https://dx.doi.org/10.48550/arxiv.1912.00982 BASE Hide details Page: 1 2

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