Update README.md

README.md

@@ -62,4 +62,20 @@ AraDPR demonstrates superior performance over traditional retrieval methods, sig
 
 ## Technical Specifications
 Model Architecture and Objective
-AraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space.
+AraDPR utilizes a dual-encoder architecture, with separate encoders for questions and passages. The model is optimized to project semantically related questions and passages closer in the vector space.
+
+
+## Citation
+
+If you find these codes or data useful, please consider citing our paper as:
+
+```
+@misc{abdallah2024arabicaqa,
+      title={ArabicaQA: A Comprehensive Dataset for Arabic Question Answering}, 
+      author={Abdelrahman Abdallah and Mahmoud Kasem and Mahmoud Abdalla and Mohamed Mahmoud and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
+      year={2024},
+      eprint={2403.17848},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```