Update citation to ACL Anthology version

README.md

@@ -36,15 +36,19 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
 This model accompanies the [BioLORD: Learning Ontological Representations from Definitions](https://arxiv.org/abs/2210.11892) paper, accepted in the EMNLP 2022 Findings. When you use this model, please cite the original paper as follows:
 
 ```latex
-@misc{https://doi.org/10.48550/arxiv.2210.11892,
-  title = {BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)},
-  author = {Remy, François and Demuynck, Kris and Demeester, Thomas},
-  url = {https://arxiv.org/abs/2210.11892},
-  doi = {10.48550/ARXIV.2210.11892},
-  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
-  publisher = {arXiv},
-  year = {2022},
-  copyright = {Creative Commons Attribution 4.0 International}
+@inproceedings{remy-etal-2022-biolord,
+    title = "{B}io{LORD}: Learning Ontological Representations from Definitions for Biomedical Concepts and their Textual Descriptions",
+    author = "Remy, François  and
+      Demuynck, Kris  and
+      Demeester, Thomas",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-emnlp.104",
+    pages = "1454--1465",
+    abstract = "This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational knowledge graph consisting of biomedical ontologies. Thanks to this grounding, our model produces more semantic concept representations that match more closely the hierarchical structure of ontologies. BioLORD establishes a new state of the art for text similarity on both clinical sentences (MedSTS) and biomedical concepts (MayoSRS).",
 }
 ```