1 code implementation • NLPerspectives (LREC) 2022 • Kamil Kanclerz, Marcin Gruza, Konrad Karanowski, Julita Bielaniewicz, Piotr Milkowski, Jan Kocon, Przemyslaw Kazienko
This supports our overall observation that personalized models should always be considered in all subjective NLP tasks, including hate speech detection.
1 code implementation • 10 Dec 2023 • Piotr Miłkowski, Konrad Karanowski, Patryk Wielopolski, Jan Kocoń, Przemysław Kazienko, Maciej Zięba
It may be solved by Personalized Natural Language Processing (PNLP), where the model exploits additional information about the reader to make more accurate predictions.
1 code implementation • Conference on Empirical Methods in Natural Language Processing (EMNLP) 2023 • Kamil Kanclerz, Konrad Karanowski, Julita Bielaniewicz, Marcin Gruza, Piotr Miłkowski, Jan Kocon, Przemyslaw Kazienko
In this paper, we present novel Personalized Active Learning techniques for Subjective NLP tasks (PALS) to either reduce the cost of the annotation process or to boost the learning effect.
no code implementations • 16 May 2022 • Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski, Tomasz Trzciński, Maciej Zięba
By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
1 code implementation • 21 Mar 2022 • Marcin Sendera, Marcin Przewięźlikowski, Konrad Karanowski, Maciej Zięba, Jacek Tabor, Przemysław Spurek
Few-shot models aim at making predictions using a minimal number of labeled examples from a given task.