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Proceedings of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 30

Using Transformer models for gender attribution in Polish

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DOI: http://dx.doi.org/10.15439/2022F197

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 7377 ()

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Abstract. Gender identification is the task of predicting thegender of an author of a given text. Some languages, including Polish, exhibit gender-revealing syntactic expression. In this paper, we investigate machine learning methods for gender identification in Polish. For the evaluation, we use large (780M words) corpus``He said she said'', created by grepping (for author's gender identification) gender-revealing syntactic expres- sions and normalizing all these expressions to masculine form (for preventing classifiers from using syntactic features). In this work, we evaluate TF-IDF based, fastText, LSTM, RoBERTa models, differentiating self-contained and non-self-contained approaches. We also provide a human baseline. We report large improvements using pre-trained RoBERTa models and discuss the possible contamination of test data for the best pre-trained model.


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