{"url":"/task/sarcasm-detection","name":"Sarcasm Detection","slug":"sarcasm-detection","description_markdown":"The goal of **Sarcasm Detection** is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.\n\n\n<span class=\"description-source\">Source: [Attentional Multi-Reading Sarcasm Detection ](https://arxiv.org/abs/1809.03051)</span>","categories":[{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":266,"papers_with_code":76,"benchmarks":9,"benchmark_tables_in_archive":9,"benchmark_tables_shown":9,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":14,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/sarcasm-detection-on-big-bench-snarks","slug":"sarcasm-detection-on-big-bench-snarks","dataset":"BIG-bench (SNARKS)","dataset_url":"/dataset/big-bench","rows_in_archive":8,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"PaLM 2(few-shot, k=3, CoT)","paper_title":"PaLM 2 Technical Report","paper_url":"/paper/palm-2-technical-report-1","paper_date":"2023-05-17","arxiv_id":"2305.10403","code_links":[{"title":"eternityyw/tram-benchmark","url":"https://github.com/eternityyw/tram-benchmark"}],"syntology":null}},{"leaderboard":"/sota/sarcasm-detection-on-figlang-2020-reddit","slug":"sarcasm-detection-on-figlang-2020-reddit","dataset":"FigLang 2020 Reddit Dataset","dataset_url":"/dataset/reddit","rows_in_archive":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"BERT+Aspect-based approaches","paper_title":"Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection","paper_url":"/paper/applying-transformers-and-aspect-based","paper_date":"2020-07-01","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/sarcasm-detection-on-figlang-2020-twitter","slug":"sarcasm-detection-on-figlang-2020-twitter","dataset":"FigLang 2020 Twitter Dataset","dataset_url":null,"rows_in_archive":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"RoBERTa_large (Context-Response)","paper_title":"Sarcasm Detection using Context Separators in Online Discourse","paper_url":"/paper/sarcasm-detection-using-context-separators-in","paper_date":"2020-06-01","arxiv_id":"2006.00850","code_links":[],"syntology":null}},{"leaderboard":"/sota/sarcasm-detection-on-sarc-all-bal","slug":"sarcasm-detection-on-sarc-all-bal","dataset":"SARC (all-bal)","dataset_url":"/dataset/sarc","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"CASCADE","paper_title":"CASCADE: Contextual Sarcasm Detection in Online Discussion Forums","paper_url":"/paper/cascade-contextual-sarcasm-detection-in","paper_date":"2018-05-16","arxiv_id":"1805.06413","code_links":[{"title":"SenticNet/CASCADE--ContextuAl-SarCAsm-DEtector","url":"https://github.com/SenticNet/CASCADE--ContextuAl-SarCAsm-DEtector"}],"syntology":null}},{"leaderboard":"/sota/sarcasm-detection-on-sarc-pol-bal","slug":"sarcasm-detection-on-sarc-pol-bal","dataset":"SARC (pol-bal)","dataset_url":"/dataset/sarc","rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Bag-of-Bigrams","paper_title":"A Large Self-Annotated Corpus for Sarcasm","paper_url":"/paper/a-large-self-annotated-corpus-for-sarcasm","paper_date":"2017-04-19","arxiv_id":"1704.05579","code_links":[{"title":"NLPrinceton/SARC","url":"https://github.com/NLPrinceton/SARC"},{"title":"chrisolen1/sarcasm-detection","url":"https://github.com/chrisolen1/sarcasm-detection"},{"title":"NauqGnesh/RedditSarcasm","url":"https://github.com/NauqGnesh/RedditSarcasm"},{"title":"Kaguura/SarcasmDetection","url":"https://github.com/Kaguura/SarcasmDetection"},{"title":"karlwbaker/Springboard_capstone","url":"https://github.com/karlwbaker/Springboard_capstone"},{"title":"sachinsharma3191/Sarcasm-Detection","url":"https://github.com/sachinsharma3191/Sarcasm-Detection"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/sarcasm-detection-on-isarcasm","slug":"sarcasm-detection-on-isarcasm","dataset":"iSarcasm","dataset_url":"/dataset/isarcasm","rows_in_archive":1,"metrics":["F1-Score"],"first_row_in_archive_order":{"model":"RoBERTa + Mutation Data Augmentation","paper_title":"UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation","paper_url":"/paper/utnlp-at-semeval-2022-task-6-a-comparative","paper_date":"2022-04-18","arxiv_id":"2204.08198","code_links":[{"title":"amirabaskohi/semeval2022-task6-sarcasm-detection","url":"https://github.com/amirabaskohi/semeval2022-task6-sarcasm-detection"},{"title":"priyank96/dataset-pruning-sarcasm-detection","url":"https://github.com/priyank96/dataset-pruning-sarcasm-detection"}],"syntology":null}},{"leaderboard":"/sota/sarcasm-detection-on-mustard","slug":"sarcasm-detection-on-mustard","dataset":"MUStARD++","dataset_url":"/dataset/mustard-1","rows_in_archive":1,"metrics":["Precision","Recall","F1"],"first_row_in_archive_order":{"model":"MUStARD++","paper_title":"A Multimodal Corpus for Emotion Recognition in Sarcasm","paper_url":"/paper/a-multimodal-corpus-for-emotion-recognition","paper_date":"2022-06-05","arxiv_id":"2206.02119","code_links":[{"title":"apoorva-nunna/mustard_plus_plus","url":"https://github.com/apoorva-nunna/mustard_plus_plus"}],"syntology":null}},{"leaderboard":"/sota/sarcasm-detection-on-sarc-pol-unbal","slug":"sarcasm-detection-on-sarc-pol-unbal","dataset":"SARC (pol-unbal)","dataset_url":"/dataset/sarc","rows_in_archive":1,"metrics":["Avg F1"],"first_row_in_archive_order":{"model":"Bag-of-Words","paper_title":"A Large Self-Annotated Corpus for Sarcasm","paper_url":"/paper/a-large-self-annotated-corpus-for-sarcasm","paper_date":"2017-04-19","arxiv_id":"1704.05579","code_links":[{"title":"NLPrinceton/SARC","url":"https://github.com/NLPrinceton/SARC"},{"title":"chrisolen1/sarcasm-detection","url":"https://github.com/chrisolen1/sarcasm-detection"},{"title":"NauqGnesh/RedditSarcasm","url":"https://github.com/NauqGnesh/RedditSarcasm"},{"title":"Kaguura/SarcasmDetection","url":"https://github.com/Kaguura/SarcasmDetection"},{"title":"karlwbaker/Springboard_capstone","url":"https://github.com/karlwbaker/Springboard_capstone"},{"title":"sachinsharma3191/Sarcasm-Detection","url":"https://github.com/sachinsharma3191/Sarcasm-Detection"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/sarcasm-detection-on-wits","slug":"sarcasm-detection-on-wits","dataset":"WITS","dataset_url":null,"rows_in_archive":1,"metrics":["R1"],"first_row_in_archive_order":{"model":"BART","paper_title":"When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues","paper_url":"/paper/when-did-you-become-so-smart-oh-wise-one-1","paper_date":"2022-03-12","arxiv_id":"2203.06419","code_links":[{"title":"lcs2-iiitd/maf","url":"https://github.com/lcs2-iiitd/maf"}],"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":2}}}],"datasets":[{"url":"/dataset/reddit","name":"Reddit","full_name":"","num_papers_in_archive":699},{"url":"/dataset/big-bench","name":"BIG-bench","full_name":"Beyond the Imitation Game Benchmark","num_papers_in_archive":349},{"url":"/dataset/sarc","name":"SARC","full_name":"","num_papers_in_archive":35},{"url":"/dataset/isarcasmeval","name":"iSarcasmEval","full_name":"","num_papers_in_archive":22},{"url":"/dataset/isarcasm","name":"iSarcasm","full_name":"iSarcasm","num_papers_in_archive":17},{"url":"/dataset/arsarcasm-v2","name":"ArSarcasm-v2","full_name":"","num_papers_in_archive":15},{"url":"/dataset/arsarcasm","name":"ArSarcasm","full_name":"","num_papers_in_archive":14},{"url":"/dataset/mustard-1","name":"MUStARD++","full_name":"","num_papers_in_archive":10},{"url":"/dataset/headlines-dataset","name":"Headlines dataset","full_name":"","num_papers_in_archive":5},{"url":"/dataset/sarcasm-corpus-v2","name":"Sarcasm Corpus V2","full_name":"","num_papers_in_archive":5},{"url":"/dataset/spirs","name":"SPIRS","full_name":"","num_papers_in_archive":3},{"url":"/dataset/mmsd2-0","name":"MMSD2.0","full_name":"Towards a Reliable Multi-modal Sarcasm Detection System","num_papers_in_archive":2},{"url":"/dataset/mustard","name":"MUStARD","full_name":"Multimodal Sarcasm Detection Dataset","num_papers_in_archive":2},{"url":"/dataset/cards-against-humanity","name":"Cards Against Humanity","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":76,"tagged_in_all":266,"items":[{"url":"/paper/using-millions-of-emoji-occurrences-to-learn","title":"Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm","date":"2017-08-01","arxiv_id":"1708.00524","repositories_listed":7,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/a-large-self-annotated-corpus-for-sarcasm","title":"A Large Self-Annotated Corpus for Sarcasm","date":"2017-04-19","arxiv_id":"1704.05579","repositories_listed":6,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/scaling-language-models-methods-analysis-1","title":"Scaling Language Models: Methods, Analysis & Insights from Training Gopher","date":"2021-12-08","arxiv_id":"2112.11446","repositories_listed":3,"syntology":null},{"url":"/paper/sarcasm-detection-using-hybrid-neural-network","title":"Sarcasm Detection using Hybrid Neural Network","date":"2019-08-20","arxiv_id":"1908.07414","repositories_listed":3,"syntology":null},{"url":"/paper/a-deeper-look-into-sarcastic-tweets-using","title":"A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks","date":"2016-10-27","arxiv_id":"1610.08815","repositories_listed":3,"syntology":null},{"url":"/paper/modelling-context-with-user-embeddings-for","title":"Modelling Context with User Embeddings for Sarcasm Detection in Social Media","date":"2016-07-04","arxiv_id":"1607.00976","repositories_listed":3,"syntology":null},{"url":"/paper/bloomberggpt-a-large-language-model-for","title":"BloombergGPT: A Large Language Model for Finance","date":"2023-03-30","arxiv_id":"2303.17564","repositories_listed":2,"syntology":null},{"url":"/paper/utnlp-at-semeval-2022-task-6-a-comparative","title":"UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation","date":"2022-04-18","arxiv_id":"2204.08198","repositories_listed":2,"syntology":null},{"url":"/paper/training-compute-optimal-large-language","title":"Training Compute-Optimal Large Language Models","date":"2022-03-29","arxiv_id":"2203.15556","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_unverified":3,"n_pointer_only":4}},{"url":"/paper/a-corpus-of-english-hindi-code-mixed-tweets","title":"A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection","date":"2018-05-30","arxiv_id":"1805.11869","repositories_listed":2,"syntology":null},{"url":"/paper/the-role-of-conversation-context-for-sarcasm","title":"The Role of Conversation Context for Sarcasm Detection in Online Interactions","date":"2017-07-19","arxiv_id":"1707.06226","repositories_listed":2,"syntology":null},{"url":"/paper/context-dependent-sentiment-analysis-in-user","title":"Context-Dependent Sentiment Analysis in User-Generated Videos","date":"2017-07-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/dual-level-adaptive-incongruity-enhanced","title":"Dual-level Adaptive Incongruity-enhanced Model for Multimodal Sarcasm Detection","date":"2025-01-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rclmufn-relational-context-learning-and","title":"RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection","date":"2024-12-17","arxiv_id":"2412.13008","repositories_listed":1,"syntology":null},{"url":"/paper/gender-bias-mitigation-for-bangla","title":"Gender Bias Mitigation for Bangla Classification Tasks","date":"2024-11-16","arxiv_id":"2411.10636","repositories_listed":1,"syntology":null},{"url":"/paper/an-innovative-cgl-mha-model-for-sarcasm","title":"An Innovative CGL-MHA Model for Sarcasm Sentiment Recognition Using the MindSpore Framework","date":"2024-11-02","arxiv_id":"2411.01264","repositories_listed":1,"syntology":null},{"url":"/paper/sarcasm-detection-in-a-less-resourced","title":"Sarcasm Detection in a Less-Resourced Language","date":"2024-10-16","arxiv_id":"2410.12704","repositories_listed":1,"syntology":null},{"url":"/paper/interclip-mep-interactive-clip-and-memory","title":"InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection","date":"2024-06-24","arxiv_id":"2406.16464","repositories_listed":1,"syntology":null},{"url":"/paper/cofipara-a-coarse-to-fine-paradigm-for","title":"CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models","date":"2024-05-01","arxiv_id":"2405.00390","repositories_listed":1,"syntology":{"n":21,"n_ran":17,"n_unverified":4,"n_pointer_only":21}},{"url":"/paper/generalizable-sarcasm-detection-is-just","title":"Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!","date":"2024-04-09","arxiv_id":"2404.06357","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-semantic-understanding-with","title":"Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment","date":"2024-03-11","arxiv_id":"2403.06355","repositories_listed":1,"syntology":null},{"url":"/paper/kocosa-korean-context-aware-sarcasm-detection","title":"KoCoSa: Korean Context-aware Sarcasm Detection Dataset","date":"2024-02-22","arxiv_id":"2402.14428","repositories_listed":1,"syntology":null},{"url":"/paper/docmsu-a-comprehensive-benchmark-for-document","title":"DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding","date":"2023-12-26","arxiv_id":"2312.16023","repositories_listed":1,"syntology":null},{"url":"/paper/mmoe-mixture-of-multimodal-interaction","title":"MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts","date":"2023-11-16","arxiv_id":"2311.09580","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":5}},{"url":"/paper/improving-multimodal-classification-of-social","title":"Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks","date":"2023-09-14","arxiv_id":"2309.07794","repositories_listed":1,"syntology":null},{"url":"/paper/a-wide-evaluation-of-chatgpt-on-affective","title":"A Wide Evaluation of ChatGPT on Affective Computing Tasks","date":"2023-08-26","arxiv_id":"2308.13911","repositories_listed":1,"syntology":null},{"url":"/paper/mmsd2-0-towards-a-reliable-multi-modal","title":"MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System","date":"2023-07-14","arxiv_id":"2307.07135","repositories_listed":1,"syntology":null},{"url":"/paper/a-big-data-approach-towards-sarcasm-detection","title":"A big data approach towards sarcasm detection in Russian","date":"2023-06-01","arxiv_id":"2306.00445","repositories_listed":1,"syntology":null},{"url":"/paper/borrowing-human-senses-comment-aware-self","title":"Borrowing Human Senses: Comment-Aware Self-Training for Social Media Multimodal Classification","date":"2023-03-27","arxiv_id":"2303.15016","repositories_listed":1,"syntology":null},{"url":"/paper/tedb-system-description-to-a-shared-task-on","title":"TEDB System Description to a Shared Task on Euphemism Detection 2022","date":"2023-01-16","arxiv_id":"2301.06602","repositories_listed":1,"syntology":null}],"syntology_records":5,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}