Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian

• URL: http://arxiv.org/abs/2206.01583v1
• Date: Fri, 3 Jun 2022 14:12:33 GMT
• Title: Findings of the The RuATD Shared Task 2022 on Artificial Text Detection in Russian
• Authors: Tatiana Shamardina, Vladislav Mikhailov, Daniil Chernianskii, Alena Fenogenova, Marat Saidov, Anastasiya Valeeva, Tatiana Shavrina, Ivan Smurov, Elena Tutubalina, Ekaterina Artemova
• Abstract summary: 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 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. The human-written texts are collected from publicly available resources across multiple domains.
• Score: 6.9244605050142995
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

Related papers

• Advacheck at GenAI Detection Task 1: AI Detection Powered by Domain-Aware Multi-Tasking [0.0]
  The paper describes a system designed by Advacheck team to recognise machine-generated and human-written texts in the monolingual subtask of GenAI Detection Task 1 competition. Our developed system is a multi-task architecture with shared Transformer between several classification heads.
  arXiv  Detail & Related papers  (2024-11-18T17:03:30Z)
• SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection [68.858931667807]
  Subtask A is a binary classification task determining whether a text is written by a human or generated by a machine. Subtask B is to detect the exact source of a text, discerning whether it is written by a human or generated by a specific LLM. Subtask C aims to identify the changing point within a text, at which the authorship transitions from human to machine.
  arXiv  Detail & Related papers  (2024-04-22T13:56:07Z)
• IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts [4.78482610709922]
  This paper describes our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness. The challenge is focused on automatically detecting the degree of relatedness between pairs of sentences for 14 languages.
  arXiv  Detail & Related papers  (2024-04-06T05:58:42Z)
• Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains [6.44756483013808]
  This paper presents the overview of the AuTexTification task as part of the IberLEF 2023 Workshop in Iberian Languages Evaluation Forum. Our AuTexTification dataset contains more than 160.000 texts across two languages (English and Spanish) and five domains (tweets, reviews, news, legal, and how-to articles) A total of 114 teams signed up to participate, of which 36 sent 175 runs, and 20 of them sent their working notes.
  arXiv  Detail & Related papers  (2023-09-20T13:10:06Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• MAGE: Machine-generated Text Detection in the Wild [82.70561073277801]
  Large language models (LLMs) have achieved human-level text generation, emphasizing the need for effective AI-generated text detection. We build a comprehensive testbed by gathering texts from diverse human writings and texts generated by different LLMs. Despite challenges, the top-performing detector can identify 86.54% out-of-domain texts generated by a new LLM, indicating the feasibility for application scenarios.
  arXiv  Detail & Related papers  (2023-05-22T17:13:29Z)
• SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes [93.19166902594168]
  We propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation. Key concept is to synthesize realistic model mistakes by perturbing sentences retrieved from a corpus. We evaluate SESCORE2 and previous methods on four text generation tasks across three languages.
  arXiv  Detail & Related papers  (2022-12-19T09:02:16Z)
• niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text [0.0]
  This paper describes the system description for the HinglishEval challenge at INLG 2022. The goal of this task was to investigate the factors influencing the quality of the code-mixed text generation system.
  arXiv  Detail & Related papers  (2022-06-17T17:36:03Z)
• Deep Learning for Text Style Transfer: A Survey [71.8870854396927]
  Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text. We present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data.
  arXiv  Detail & Related papers  (2020-11-01T04:04:43Z)
• Robust Conversational AI with Grounded Text Generation [77.56950706340767]
  GTG is a hybrid model which uses a large-scale Transformer neural network as its backbone. It generates responses grounded in dialog belief state and real-world knowledge for task completion.
  arXiv  Detail & Related papers  (2020-09-07T23:49:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.