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Between History and Natural Language Processing: Study, Enrichment and Online Publication of French Parliamentary Debates of the Early Third Republic (1881-1899)
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In: ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora ; https://hal.archives-ouvertes.fr/hal-03623351 ; ParlaCLARIN III at LREC2022 - Workshop on Creating, Enriching and Using Parliamentary Corpora, Jun 2022, Marseille, France ; https://www.clarin.eu/ParlaCLARIN-III (2022)
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Ensemble of Opinion Dynamics Models to Understand the Role of the Undecided in the Vaccination Debate ...
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Zum Ungleichgewicht digital vermittelten Sachunterrichts und sprachlich-kommunikativer Anforderungen ...
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Zum Ungleichgewicht digital vermittelten Sachunterrichts und sprachlich-kommunikativer Anforderungen
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In: Sachunterricht in der Informationsgesellschaft. Bad Heilbrunn : Verlag Julius Klinkhardt 2022, S. 114-121. - (Probleme und Perspektiven des Sachunterrichts; 32) (2022)
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Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers ...
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MMTAfrica: Multilingual Machine Translation for African Languages ...
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A New Generation of Perspective API: Efficient Multilingual Character-level Transformers ...
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MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset ...
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Korean Online Hate Speech Dataset for Multilabel Classification: How Can Social Science Improve Dataset on Hate Speech? ...
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Quantifying knowledge synchronisation in the 21st century ...
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An NLP Solution to Foster the Use of Information in Electronic Health Records for Efficiency in Decision-Making in Hospital Care ...
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Networks and Identity Drive Geographic Properties of the Diffusion of Linguistic Innovation ...
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Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study ...
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Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations ...
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Achieving Downstream Fairness with Geometric Repair ...
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Abstract:
Consider a scenario where some upstream model developer must train a fair model, but is unaware of the fairness requirements of a downstream model user or stakeholder. In the context of fair classification, we present a technique that specifically addresses this setting, by post-processing a regressor's scores such they yield fair classifications for any downstream choice in decision threshold. To begin, we leverage ideas from optimal transport to show how this can be achieved for binary protected groups across a broad class of fairness metrics. Then, we extend our approach to address the setting where a protected attribute takes on multiple values, by re-recasting our technique as a convex optimization problem that leverages lexicographic fairness. ...
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Keyword:
Computers and Society cs.CY; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://arxiv.org/abs/2203.07490 https://dx.doi.org/10.48550/arxiv.2203.07490
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Towards Responsible Natural Language Annotation for the Varieties of Arabic ...
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Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models ...
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Who will share Fake-News on Twitter? Psycholinguistic cues in online post histories discriminate Between actors in the misinformation ecosystem ...
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