Update README.md

README.md

@@ -16,10 +16,20 @@ SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained wi
 
 ### Citation
 ```bibtex
-@article{liu2020self,
-  title={Self-alignment Pre-training for Biomedical Entity Representations},
-  author={Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel},
-  journal={arXiv preprint arXiv:2010.11784},
-  year={2020}
+@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.",
 }
 ```