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    <abstract>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, neural-based, 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.</abstract>
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%0 Conference Proceedings
%T Detecting Text Formality: A Study of Text Classification Approaches
%A Dementieva, Daryna
%A Babakov, Nikolay
%A Panchenko, Alexander
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F dementieva-etal-2023-detecting
%X 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, neural-based, 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.
%U https://aclanthology.org/2023.ranlp-1.31
%P 274-284

Markdown (Informal)

[Detecting Text Formality: A Study of Text Classification Approaches](https://aclanthology.org/2023.ranlp-1.31) (Dementieva et al., RANLP 2023)

• Detecting Text Formality: A Study of Text Classification Approaches (Dementieva et al., RANLP 2023)

ACL

• Daryna Dementieva, Nikolay Babakov, and Alexander Panchenko. 2023. Detecting Text Formality: A Study of Text Classification Approaches. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 274–284, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.