Update README.md

README.md

@@ -52,19 +52,10 @@ The inference time(Min:Sec) and memory(GiB) for each model on 2.8K documents. Av
 
 ### Citation
 
-```
-@inproceedings{otmazgin-etal-2022-f,
-    title = "{F}-coref: Fast, Accurate and Easy to Use Coreference Resolution",
-    author = "Otmazgin, Shon  and
-      Cattan, Arie  and
-      Goldberg, Yoav",
-    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing: System Demonstrations",
-    month = nov,
-    year = "2022",
-    address = "Taipei, Taiwan",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.aacl-demo.6",
-    pages = "48--56",
-    abstract = "We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode based on the LingMess architecture, providing state-of-the-art coreference accuracy, and a substantially faster model, F-coref, which is the focus of this work. F-coref allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover batching. https://github.com/shon-otmazgin/fastcoref",
+@inproceedings{Otmazgin2022FcorefFA,
+  title={F-coref: Fast, Accurate and Easy to Use Coreference Resolution},
+  author={Shon Otmazgin and Arie Cattan and Yoav Goldberg},
+  booktitle={AACL},
+  year={2022}
 }
 ```