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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 30 1 USCORE: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation ... Belouadi, Jonas; Eger, Steffen. - : arXiv, 2022 BASE Show details 2 Constrained Density Matching and Modeling for Cross-lingual Alignment of Contextualized Representations ... Zhao, Wei; Eger, Steffen. - : arXiv, 2022 BASE Show details 3 Towards Explainable Evaluation Metrics for Natural Language Generation ... Leiter, Christoph; Lertvittayakumjorn, Piyawat; Fomicheva, Marina. - : arXiv, 2022 BASE Show details 4 End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Eger, Steffen. - : Underline Science Inc., 2021 BASE Show details 5 Better than Average: Paired Evaluation of NLP systems ... 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; Eger, Steffen; Peyrard, Maxime. - : Underline Science Inc., 2021 BASE Show details 6 Changes in European Solidarity Before and During COVID-19: Evidence from a Large Crowd- and Expert-Annotated Twitter Dataset ... 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; Eger, Steffen; Grunow, Daniela. - : Underline Science Inc., 2021 BASE Show details 7 BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks ... Keller, Yannik; Mackensen, Jan; Eger, Steffen. - : arXiv, 2021 BASE Show details 8 Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ... Kaster, Marvin; Zhao, Wei; Eger, Steffen. - : arXiv, 2021 BASE Show details 9 Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Eger, Steffen; Kaster, Marvin. - : Underline Science Inc., 2021 BASE Show details 10 Inducing Language-Agnostic Multilingual Representations ... Zhao, Wei; Eger, Steffen; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 11 Probing Multilingual BERT for Genetic and Typological Signals ... Rama, Taraka; Beinborn, Lisa; Eger, Steffen. - : arXiv, 2020 BASE Show details 12 On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation ... Zhao, Wei; Glavaš, Goran; Peyrard, Maxime. - : arXiv, 2020 BASE Show details 13 How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation ... Eger, Steffen; Daxenberger, Johannes; Gurevych, Iryna. - : arXiv, 2020 Abstract: Sentence encoders map sentences to real valued vectors for use in downstream applications. To peek into these representations - e.g., to increase interpretability of their results - probing tasks have been designed which query them for linguistic knowledge. However, designing probing tasks for lesser-resourced languages is tricky, because these often lack large-scale annotated data or (high-quality) dependency parsers as a prerequisite of probing task design in English. To investigate how to probe sentence embeddings in such cases, we investigate sensitivity of probing task results to structural design choices, conducting the first such large scale study. We show that design choices like size of the annotated probing dataset and type of classifier used for evaluation do (sometimes substantially) influence probing outcomes. We then probe embeddings in a multilingual setup with design choices that lie in a 'stable region', as we identify for English, and find that results on English do not transfer to other ... : Accepted for Publication at CONLL 2020 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2006.09109 https://dx.doi.org/10.48550/arxiv.2006.09109 BASE Hide details 14 Vec2Sent: Probing Sentence Embeddings With Natural Language Generation ... The 28th International Conference on Computational Linguistics 2020; Eger, Steffen; Kerscher, Martin. - : Underline Science Inc., 2020 BASE Show details 15 From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks ... Eger, Steffen; Benz, Yannik. - : arXiv, 2020 BASE Show details 16 On the limitations of cross-lingual encoders as exposed by reference-free machine translation evaluation Zhao, Wei; Glavaš, Goran; Peyrard, Maxime. - : Association for Computational Linguistics, 2020 BASE Show details 17 On aligning OpenIE extractions with Knowledge Bases: A case study Gashteovski, Kiril; Gemulla, Rainer; Kotnis, Bhushan. - : Association for Computational Linguistics (ACL), 2020 BASE Show details 18 Semantic Change and Emerging Tropes In a Large Corpus of New High German Poetry ... Haider, Thomas; Eger, Steffen. - : arXiv, 2019 BASE Show details 19 Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need! ... Eger, Steffen; Daxenberger, Johannes; Stab, Christian. - : arXiv, 2018 BASE Show details 20 What is the Essence of a Claim? Cross-Domain Claim Identification ... Daxenberger, Johannes; Eger, Steffen; Habernal, Ivan. - : arXiv, 2017 BASE Show details Page: 1 2

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