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@@ -18,11 +18,20 @@ SapBERT [(Liu et al. 2020)](https://arxiv.org/pdf/2010.11784.pdf) trained with [
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  ### Citation
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  ```bibtex
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- @article{liu2020self,
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- title={Self-alignment Pre-training for Biomedical Entity Representations},
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- author={Liu, Fangyu and Shareghi, Ehsan and Meng, Zaiqiao and Basaldella, Marco and Collier, Nigel},
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- journal={arXiv preprint arXiv:2010.11784},
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- year={2020}
 
 
 
 
 
 
 
 
 
 
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  }
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-
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  ```
 
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  ### Citation
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  ```bibtex
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+ @inproceedings{liu-etal-2021-self,
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+ title = "Self-Alignment Pretraining for Biomedical Entity Representations",
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+ author = "Liu, Fangyu and
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+ Shareghi, Ehsan and
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+ Meng, Zaiqiao and
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+ Basaldella, Marco and
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+ Collier, Nigel",
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+ booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
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+ month = jun,
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+ year = "2021",
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+ address = "Online",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://www.aclweb.org/anthology/2021.naacl-main.334",
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+ pages = "4228--4238",
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+ 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.",
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  }
 
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  ```