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Improved statistical machine translation using monolingual paraphrases ...
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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ...
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Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic languages ...
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A Neighbourhood Framework for Resource-Lean Content Flagging ...
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Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training ...
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SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images ...
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SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification ...
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SUper Team at SemEval-2016 Task 3: Building a feature-rich system for community question answering ...
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Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields ...
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RuleBERT: Teaching Soft Rules to Pre-Trained Language Models ...
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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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On a Novel Application of Wasserstein-Procrustes for Unsupervised Cross-Lingual Learning ...
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EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering ...
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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) ...
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What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context ...
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SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles ...
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Abstract:
We present the results and the main findings of SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. The task featured two subtasks. Subtask SI is about SpanIdentification: given a plain-text document, spot the specific text fragments containing propaganda. Subtask TC is about Technique Classification: given a specific text fragment, in the context of a full document, determine the propaganda technique it uses, choosing from an inventory of 14 possible propaganda techniques. The task attracted a large number of participants: 250teams signed up to participate and 44 made a submission on the test set. In this paper, we present the task, analyze the results, and discuss the system submissions and the methods they used. For both subtasks, the best systems used pre-trained Transformers and ensembles. ...
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Keyword:
Computer and Information Science; Natural Language Processing; Neural Network
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URL: https://dx.doi.org/10.48448/s1fa-v223 https://underline.io/lecture/6413-semeval-2020-task-11-detection-of-propaganda-techniques-in-news-articles
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