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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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Detecting Harmful Memes and Their Targets ...
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Abstract:
Read paper: https://www.aclanthology.org/2021.findings-acl.246 Abstract: Among the various modes of communication in social media, the use of Internet memes has emerged as a powerful means to convey political, psychological, and socio-cultural opinions. Although memes are typically humorous in nature, recent days have witnessed a proliferation of harmful memes targeted to abuse various social entities. As most harmful memes are highly satirical and abstruse without appropriate contexts, off-the-shelf multimodal models may not be adequate to understand their underlying semantics. In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target. To this end, we present HarMeme, the first benchmark dataset, containing 3,544 memes related to COVID-19. Each meme went through a rigorous two-stage annotation process. In the first stage, we labeled a meme as very harmful, partially harmful, or harmless; in the second stage, we further annotated ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; Deep Learning; Electromagnetism; FOS Physical sciences; Information and Knowledge Engineering; Neural Network; Semantics
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URL: https://dx.doi.org/10.48448/803b-hv14 https://underline.io/lecture/26337-detecting-harmful-memes-and-their-targets
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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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