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 • 13 Dec 2023 • Kamil Kanclerz, Julita Bielaniewicz, Marcin Gruza, Jan Kocon, Stanisław Woźniak, Przemysław Kazienko
Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model.
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.
1 code implementation • 21 Feb 2023 • Jan Kocoń, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydło, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, Anna Kocoń, Bartłomiej Koptyra, Wiktoria Mieleszczenko-Kowszewicz, Piotr Miłkowski, Marcin Oleksy, Maciej Piasecki, Łukasz Radliński, Konrad Wojtasik, Stanisław Woźniak, Przemysław Kazienko
Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation.
no code implementations • WASSA (ACL) 2022 • Krzysztof Rajda, Łukasz Augustyniak, Piotr Gramacki, Marcin Gruza, Szymon Woźniak, Tomasz Kajdanowicz
We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included results on the internally annotated datasets.
1 code implementation • ACL 2021 • Piotr Milkowski, Marcin Gruza, Kamil Kanclerz, Przemyslaw Kazienko, Damian Grimling, Jan Kocon
Analysis of emotions elicited by opinions, comments, or articles commonly exploits annotated corpora, in which the labels assigned to documents average the views of all annotators, or represent a majority decision.
1 code implementation • ACL 2021 • Kamil Kanclerz, Alicja Figas, Marcin Gruza, Tomasz Kajdanowicz, Jan Kocon, Daria Puchalska, Przemyslaw Kazienko
There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers.