Update README.md

README.md

@@ -45,4 +45,34 @@ For further information on the languages and data, see https://huggingface.co/da
 
 * id: unique id of the document (from the Oscar dataset)
 * labels: the list of labels assigned to the text
-* text: the original text of the document (as appears in the Oscar dataset)
+* text: the original text of the document (as appears in the Oscar dataset)
+
+
+### Citing
+
+```
+@inproceedings{laippala-etal-2022-towards,
+    title = "Towards better structured and less noisy Web data: Oscar with Register annotations",
+    author = {Laippala, Veronika  and
+      Salmela, Anna  and
+      R{\"o}nnqvist, Samuel  and
+      Aji, Alham Fikri  and
+      Chang, Li-Hsin  and
+      Dhifallah, Asma  and
+      Goulart, Larissa  and
+      Kortelainen, Henna  and
+      P{\`a}mies, Marc  and
+      Prina Dutra, Deise  and
+      Skantsi, Valtteri  and
+      Sutawika, Lintang  and
+      Pyysalo, Sampo},
+    booktitle = "Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.wnut-1.23",
+    pages = "215--221",
+    abstract = {Web-crawled datasets are known to be noisy, as they feature a wide range of language use covering both user-generated and professionally edited content as well as noise originating from the crawling process. This article presents one solution to reduce this noise by using automatic register (genre) identification -whether the texts are, e.g., forum discussions, lyrical or how-to pages. We apply the multilingual register identification model by R{\"o}nnqvist et al. (2021) and label the widely used Oscar dataset. Additionally, we evaluate the model against eight new languages, showing that the performance is comparable to previous findings on a restricted set of languages. Finally, we present and apply a machine learning method for further cleaning text files originating from Web crawls from remains of boilerplate and other elements not belonging to the main text of the Web page. The register labeled and cleaned dataset covers 351 million documents in 14 languages and is available at https://huggingface.co/datasets/TurkuNLP/register{\_}oscar.},
+}
+```