Update README.md

README.md

@@ -80,6 +80,27 @@ batch = tokenizer.encode('ты супер', return_tensors='pt')
 model(batch)
 ```
 
+## Citation
+
+```
+@inproceedings{dementieva-etal-2023-detecting,
+    title = "Detecting Text Formality: A Study of Text Classification Approaches",
+    author = "Dementieva, Daryna  and
+      Babakov, Nikolay  and
+      Panchenko, Alexander",
+    editor = "Mitkov, Ruslan  and
+      Angelova, Galia",
+    booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
+    month = sep,
+    year = "2023",
+    address = "Varna, Bulgaria",
+    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
+    url = "https://aclanthology.org/2023.ranlp-1.31",
+    pages = "274--284",
+    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.",
+}
+```
+
 
 ## Licensing Information