--- dataset_info: features: - name: user dtype: string - name: label dtype: string - name: timestamp dtype: string - name: source dtype: string - name: subreddit dtype: string - name: id_original dtype: string - name: text dtype: string - name: parent_id_original dtype: string - name: parent_text dtype: string - name: Language_instance dtype: string - name: Language_variety dtype: string - name: Age dtype: string - name: Sex dtype: string - name: Ethnicity simplified dtype: string - name: Country of birth dtype: string - name: Country of residence dtype: string - name: Nationality dtype: string - name: Language_annotator dtype: string - name: Student status dtype: string - name: Employment status dtype: string splits: - name: train num_bytes: 7299373 num_examples: 14172 download_size: 1038853 dataset_size: 7299373 --- # EPIC_Irony - paper: [EPIC: Multi-Perspective Annotation of a Corpus of Irony](https://assets.amazon.science/40/b4/0f6ec06a4a33a44485de1b2b57c7/epic-multi-perspective-annotation-of-a-corpus-of-irony.pdf) at ACL 2023 Key features: - EPIC (English Perspectivist Irony Corpus) is an annotated corpus for irony analysis based on data perspectivism principles. - The corpus contains social media conversations in five regional varieties of English, annotated by contributors from corresponding countries. - The dataset explores the perspectives of annotators, taking into account their origin, age, and gender. - Perspective-aware models were created to validate EPIC, and these proved more effective and confident in identifying irony than non-perspectivist models. - The models showcase variation in irony perception across different demographic groups. - EPIC serves as a valuable resource for training perspective-aware models for irony detection. Metadata in Creative Language Toolkit ([CLTK](https://github.com/liyucheng09/cltk)) - CL Type: Irony - Task Type: detection - Size: 14k - Created time: 2023