Update README.md

README.md

@@ -85,4 +85,23 @@ For examples on how these models can be used in code, take a look at: https://gi
 
 The models are trained on Wikipedia data, which may not represent all varieties of Arabic equally. The diacritization process, while state-of-the-art, may introduce some errors or biases in the training data.
 
-The subword tokenization approach used in the mlm_p2g_non_diacritics model has limitations for phonemic modeling as noted above.
+The subword tokenization approach used in the mlm_p2g_non_diacritics model has limitations for phonemic modeling as noted above.
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@article{catt2024,
+  title={CATT: Character-based Arabic Tashkeel Transformer},
+  author={Alasmary, Faris and Zaafarani, Orjuwan and Ghannam, Ahmad},
+  journal={arXiv preprint arXiv:2407.03236},
+  year={2024}
+}
+
+@article{plbert2023,
+  title={Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions},
+  author={Li, Yinghao Aaron and Han, Cong and Jiang, Xilin and Mesgarani, Nima},
+  journal={arXiv preprint arXiv:2301.08810},
+  year={2023}
+}
+```