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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 – 11 of 11 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 BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks ... Keller, Yannik; Mackensen, Jan; Eger, Steffen. - : arXiv, 2021 Abstract: Adversarial attacks expose important blind spots of deep learning systems. While word- and sentence-level attack scenarios mostly deal with finding semantic paraphrases of the input that fool NLP models, character-level attacks typically insert typos into the input stream. It is commonly thought that these are easier to defend via spelling correction modules. In this work, we show that both a standard spellchecker and the approach of Pruthi et al. (2019), which trains to defend against insertions, deletions and swaps, perform poorly on the character-level benchmark recently proposed in Eger and Benz (2020) which includes more challenging attacks such as visual and phonetic perturbations and missing word segmentations. In contrast, we show that an untrained iterative approach which combines context-independent character-level information with context-dependent information from BERT's masked language modeling can perform on par with human crowd-workers from Amazon Mechanical Turk (AMT) supervised via 3-shot ... : Findings of ACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2106.01452 https://arxiv.org/abs/2106.01452 BASE Hide details 5 Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors ... Kaster, Marvin; Zhao, Wei; Eger, Steffen. - : arXiv, 2021 BASE Show details 6 Inducing Language-Agnostic Multilingual Representations ... Zhao, Wei; Eger, Steffen; Bjerva, Johannes. - : arXiv, 2020 BASE Show details 7 Probing Multilingual BERT for Genetic and Typological Signals ... Rama, Taraka; Beinborn, Lisa; Eger, Steffen. - : arXiv, 2020 BASE Show details 8 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 9 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 BASE Show details 10 From Hero to Zéroe: A Benchmark of Low-Level Adversarial Attacks ... Eger, Steffen; Benz, Yannik. - : arXiv, 2020 BASE Show details 11 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

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