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2 Annals of Computer Science and Information Systems

2 2022

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Using Transformer models for gender attribution in Polish

Kaczmarek Karol, Pokrywka Jakub, Graliński Filip

Annals of Computer Science and Information Systems

2022

Vol. 30

73--77

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.

Temporal Language Modeling for Short Text Document Classification with Transformers

Pokrywka Jakub, Graliński Filip

Annals of Computer Science and Information Systems

2022

Vol. 30

121--128

Language models are typically trained on solely text data, not utilizing document timestamps, which are available in most internet corpora. In this paper, we examine the impact of incorporating timestamp into transformer language model in terms of downstream classification task and masked language modeling on 2 short texts corpora. We examine different timestamp components: day of the month, month, year, weekday. We test different methods of incorporating date into the model: prefixing date components into text input and adding trained date embeddings. Our study shows, that such a temporal language model performs better than a regular language model for both documents from training data time span and unseen time span. That holds true for classification and language modeling. Prefixing date components into text performs no worse than training special date components embeddings.