Detecting Text Formality: A Study of Text Classification Approaches
Daryna Dementieva, Nikolay Babakov, Alexander Panchenko · 2023
Formality is one of the important characteristics of text documents.The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks.Before, two large-scale datasets were introduced for multiple languages featuring formality annotation-GYAFC and X-FORMAL.However, they were primarily used for the training of style transfer models.At the same time, the detection of text formality on its own may also be a useful application.This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neuralbased, and Transformer-based machine learning methods and delivers the best-performing models for public usage.We conducted three types of experiments -monolingual, multilingual, and cross-lingual.The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.