Despite different countries and social platform regulations, digital abusive speech persists as a significant challenge. One of the ways to tackle abusive, or more specifically, toxic language can be automatic text detoxification—a text style transfer task (TST) of changing register of text from toxic to more non-toxic. We extend our previous Multilingual Text Detoxification (TextDetox) task to new languages—Italian, French, Hebrew, Hinglish, Japanese, and Tatar—suggesting participants to participate in multi-lingual and cross-lingual text detoxification challenges. We provide insights into new data collection, evaluation metrics, as well as dive into the participants results. Warning: This paper contains rude text that serve only the purpose of illustrative examples.
Overview of the multilingual text detoxification task at PAN 2025
| Daryna Dementieva, Vitaly Protasov, Nikolay Babakov, Naquee Rizwan, Ilseyar Alimova, Caroline Brun, Vasily Konovalov, Arianna Muti, Chaya Liebeskind, Marina Litvak, Debora Nozza, Shehryaar Shah Khan, Sotaro Takeshita, Natalia Vanetik, Abinew Ali Ayele, Florian Schneider, Xintong Wang, Seid Muhie Yimam, Ashraf Elnagar, Animesh Mukherjee, Alexander Panchenko |
| Conference and Labs of the Evaluation Forum (CLEF), Madrid, Spain, 9-12 September, 2025 |
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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