{"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/findings-of-the-the-ruatd-shared-task-2022-on","title":"Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian","arxiv_id":"2206.01583","date":"2022-06-03","proceeding":null,"authors":["Tatiana Shamardina","Vladislav Mikhailov","Daniil Chernianskii","Alena Fenogenova","Marat Saidov","Anastasiya Valeeva","Tatiana Shavrina","Ivan Smurov","Elena Tutubalina","Ekaterina Artemova"],"abstract":"We present the shared task on artificial text detection in Russian, which is organized as a part of the Dialogue Evaluation initiative, held in 2022. The shared task dataset includes texts from 14 text generators, i.e., one human writer and 13 text generative models fine-tuned for one or more of the following generation tasks: machine translation, paraphrase generation, text summarization, text simplification. We also consider back-translation and zero-shot generation approaches. The human-written texts are collected from publicly available resources across multiple domains. The shared task consists of two sub-tasks: (i) to determine if a given text is automatically generated or written by a human; (ii) to identify the author of a given text. The first task is framed as a binary classification problem. The second task is a multi-class classification problem. We provide count-based and BERT-based baselines, along with the human evaluation on the first sub-task. A total of 30 and 8 systems have been submitted to the binary and multi-class sub-tasks, correspondingly. Most teams outperform the baselines by a wide margin. We publicly release our codebase, human evaluation results, and other materials in our GitHub repository (https://github.com/dialogue-evaluation/RuATD).","url_abs":"https://arxiv.org/abs/2206.01583v1","url_pdf":"https://arxiv.org/pdf/2206.01583v1.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":[{"paper_slug":"findings-of-the-the-ruatd-shared-task-2022-on","repo_url":"https://github.com/dialogue-evaluation/ruatd","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"dialogue-evaluation","task_name":"Dialogue Evaluation"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"paraphrase-generation","task_name":"Paraphrase Generation"},{"task_slug":"text-detection","task_name":"Text Detection"},{"task_slug":"text-simplification","task_name":"Text Simplification"},{"task_slug":"text-summarization","task_name":"Text Summarization"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2206.01583","atlas_url":"https://app.syntology.ai/?focus=2206.01583","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}