Update README.md

README.md

@@ -12,13 +12,23 @@ CC BY-SA 4.0
 ## Citation
 If you use this model, please cite the following paper:
 ```
-@misc{rybak2024transferring,
-      title={Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching}, 
-      author={Piotr Rybak},
-      year={2024},
-      eprint={2402.14408},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
+@inproceedings{rybak-2024-transferring-bert,
+    title = "Transferring {BERT} Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching",
+    author = "Rybak, Piotr",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.1456",
+    pages = "16745--16750",
+    abstract = "Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However, a significant challenge remains for low-resource languages, where limited data hinders the effective training of such models. This work presents a novel approach to bridge this gap by transferring BERT capabilities from high-resource to low-resource languages using vocabulary matching. We conduct experiments on the Silesian and Kashubian languages and demonstrate the effectiveness of our approach to improve the performance of BERT models even when the target language has minimal training data. Our results highlight the potential of the proposed technique to effectively train BERT models for low-resource languages, thus democratizing access to advanced language understanding models.",
 }
 ```