Update README.md

README.md

@@ -72,10 +72,6 @@ ConfliBERT has shown improved performance on several benchmarks relevant to its
 ## Citation
 If you use ConfliBERT in your research, please cite the following paper:
 ```bibtex
-@inproceedings{hu2022conflibert,
-  title={Conflibert: A pre-trained language model for political conflict and violence},
-  author={Hu, Yibo and Hosseini, MohammadSaleh and Skorupa Parolin, Erick and Osorio, Javier and Khan, Latifur and Brandt, Patrick and D’Orazio, Vito},
-  year={2022},
-  organization={Association for Computational Linguistics}
-}
+@article{osti_10470310,
+place = {Country unknown/Code not available}, title = {ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence}, url = {https://par.nsf.gov/biblio/10470310}, DOI = {10.18653/v1/2022.naacl-main.400}, abstractNote = {Analyzing conflicts and political violence around the world is a persistent challenge in the political science and policy communities due in large part to the vast volumes of specialized text needed to monitor conflict and violence on a global scale. To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained language model for conflict and political violence. We first gather a large domain-specific text corpus for language modeling from various sources. We then build ConfliBERT using two approaches: pre-training from scratch and continual pre-training. To evaluate ConfliBERT, we collect 12 datasets and implement 18 tasks to assess the models’ practical application in conflict research. Finally, we evaluate several versions of ConfliBERT in multiple experiments. Results consistently show that ConfliBERT outperforms BERT when analyzing political violence and conflict.}, journal = {}, publisher = {Association for Computational Linguistics}, author = {Hu, Yibo and Hosseini, MohammadSaleh and Skorupa Parolin, Erick and Osorio, Javier and Khan, Latifur and Brandt, Patrick and D’Orazio, Vito}, }
 ```