Update README.md

README.md

@@ -113,7 +113,29 @@ Being extracted and scraped from variety of internet sources, Personal and sensi
 
 ### Citation Information
 
-[More Information Needed]
+If you use this dataset in your research, please cite:
+
+```bibtex
+@inproceedings{maskey-etal-2022-nepali,
+    title = "{N}epali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for {N}epali Text Classification",
+    author = "Maskey, Utsav  and
+      Bhatta, Manish  and
+      Bhatt, Shiva  and
+      Dhungel, Sanket  and
+      Bal, Bal Krishna",
+    editor = "Melero, Maite  and
+      Sakti, Sakriani  and
+      Soria, Claudia",
+    booktitle = "Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages",
+    month = jun,
+    year = "2022",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2022.sigul-1.14/",
+    pages = "106--111",
+    abstract = "Language model pre-training has significantly impacted NLP and resulted in performance gains on many NLP-related tasks, but comparative study of different approaches on many low-resource languages seems to be missing. This paper attempts to investigate appropriate methods for pretraining a Transformer-based model for the Nepali language. We focus on the language-specific aspects that need to be considered for modeling. Although some language models have been trained for Nepali, the study is far from sufficient. We train three distinct Transformer-based masked language models for Nepali text sequences: distilbert-base (Sanh et al., 2019) for its efficiency and minuteness, deberta-base (P. He et al., 2020) for its capability of modeling the dependency of nearby token pairs and XLM-ROBERTa (Conneau et al., 2020) for its capabilities to handle multilingual downstream tasks. We evaluate and compare these models with other Transformer-based models on a downstream classification task with an aim to suggest an effective strategy for training low-resource language models and their fine-tuning."
+}
+```
 
 ### Contributions