{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semeval-2023-task-9-multilingual-tweet","title":"SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis","arxiv_id":"2210.01108","date":"2022-10-03","proceeding":null,"authors":["Jiaxin Pei","Vítor Silva","Maarten Bos","Yozon Liu","Leonardo Neves","David Jurgens","Francesco Barbieri"],"abstract":"We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular multilingual pre-trained language models. The dataset is released along with the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis (https://sites.google.com/umich.edu/semeval-2023-tweet-intimacy).","url_abs":"https://arxiv.org/abs/2210.01108v2","url_pdf":"https://arxiv.org/pdf/2210.01108v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"mint","name":"Mint","full_name":"Multilingual Intimacy analysis"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.01108","atlas_url":"https://app.syntology.ai/?focus=2210.01108","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}