Update README.md

README.md

@@ -18,20 +18,11 @@ SapBERT [(Liu et al. 2020)](https://arxiv.org/pdf/2010.11784.pdf) trained with [
 ### Citation
 
 ```bibtex
-@inproceedings{liu-etal-2021-self,
-    title = "Self-Alignment Pretraining for Biomedical Entity Representations",
-    author = "Liu, Fangyu  and
-      Shareghi, Ehsan  and
-      Meng, Zaiqiao  and
-      Basaldella, Marco  and
-      Collier, Nigel",
-    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
-    month = jun,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2021.naacl-main.334",
-    pages = "4228--4238",
-    abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.",
+@inproceedings{liu2021learning,
+	title={Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking},
+	author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
+	booktitle={Proceedings of ACL-IJCNLP 2021},
+	month = aug,
+	year={2021}
 }
 ```