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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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Abstract:
A limited amount of studies investigates the role of model-agnostic adversarial behavior in toxic content classification. As toxicity classifiers predominantly rely on lexical cues, (deliberately) creative and evolving language-use can be detrimental to the utility of current corpora and state-of-the-art models when they are deployed for content moderation. The less training data is available, the more vulnerable models might become. This study is, to our knowledge, the first to investigate the effect of adversarial behavior and augmentation for cyberbullying detection. We demonstrate that model-agnostic lexical substitutions significantly hurt classifier performance. Moreover, when these perturbed samples are used for augmentation, we show models become robust against word-level perturbations at a slight trade-off in overall task performance. Augmentations proposed in prior work on toxicity prove to be less effective. Our results underline the need for such evaluations in online harm areas with small ... : Submitted to LREC 2022 ...
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Keyword:
Computation and Language cs.CL; Computers and Society cs.CY; FOS Computer and information sciences; Social and Information Networks cs.SI
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URL: https://arxiv.org/abs/2201.06384 https://dx.doi.org/10.48550/arxiv.2201.06384
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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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