Using Transformer models for gender attribution in Polish

• Autorzy: Kaczmarek Karol , Pokrywka Jakub , Graliński Filip

Identyfikatory

DOI

10.15439/2022F197

• Języki publikacji: EN

Abstrakty

EN

Gender identification is the task of predicting the gender 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 expressions 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.

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 73--77
• Opis fizyczny: Bibliogr. 29 poz.

Twórcy

autor

Kaczmarek Karol

autor

Pokrywka Jakub

autor

Graliński Filip