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README.md

@@ -54,25 +54,25 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 ```bibtex
 
 @inproceedings{cohan-etal-2020-specter,
-    title = "{SPECTER}: Document-level Representation Learning using Citation-informed Transformers",
-    author = "Cohan, Arman  and
-      Feldman, Sergey  and
-      Beltagy, Iz  and
-      Downey, Doug  and
-      Weld, Daniel",
-    editor = "Jurafsky, Dan  and
-      Chai, Joyce  and
-      Schluter, Natalie  and
-      Tetreault, Joel",
-    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
-    month = jul,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2020.acl-main.207",
-    doi = "10.18653/v1/2020.acl-main.207",
-    pages = "2270--2282",
-    abstract = "Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.",
+  abstract = {Representation learning is a critical ingredient for natural language processing systems. Recent Transformer language models like BERT learn powerful textual representations, but these models are targeted towards token- and sentence-level training objectives and do not leverage information on inter-document relatedness, which limits their document-level representation power. For applications on scientific documents, such as classification and recommendation, accurate embeddings of documents are a necessity. We propose SPECTER, a new method to generate document-level embedding of scientific papers based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph. Unlike existing pretrained language models, Specter can be easily applied to downstream applications without task-specific fine-tuning. Additionally, to encourage further research on document-level models, we introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation. We show that Specter outperforms a variety of competitive baselines on the benchmark.},
+  address = {Online},
+  author = {Cohan, Arman  and
+Feldman, Sergey  and
+Beltagy, Iz  and
+Downey, Doug  and
+Weld, Daniel},
+  booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+  doi = {10.18653/v1/2020.acl-main.207},
+  editor = {Jurafsky, Dan  and
+Chai, Joyce  and
+Schluter, Natalie  and
+Tetreault, Joel},
+  month = jul,
+  pages = {2270--2282},
+  publisher = {Association for Computational Linguistics},
+  title = {{SPECTER}: Document-level Representation Learning using Citation-informed Transformers},
+  url = {https://aclanthology.org/2020.acl-main.207},
+  year = {2020},
 }