Update bibtex

README.md

@@ -437,9 +437,6 @@ Please be aware that this contains unfiltered data from the internet, and may co
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
 Please cite the paper if you use this dataset.  
-Until the ACL Anthology is updated with ACL 2024 papers, you can use the following BibTeX:
-
-<!-- Update with ACL Anthology bibtex-->
 ```
 @inproceedings{pal-etal-2024-document,
     title = "Document-Level Machine Translation with Large-Scale Public Parallel Corpora",
@@ -454,8 +451,10 @@ Until the ACL Anthology is updated with ACL 2024 papers, you can use the followi
     year = "2024",
     address = "Bangkok, Thailand",
     publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2024.acl-long.712",
+    url = "https://aclanthology.org/2024.acl-long.712/",
+    doi = "10.18653/v1/2024.acl-long.712",
     pages = "13185--13197",
+    abstract = "Despite the fact that document-level machine translation has inherent advantages over sentence-level machine translation due to additional information available to a model from document context, most translation systems continue to operate at a sentence level. This is primarily due to the severe lack of publicly available large-scale parallel corpora at the document level. We release a large-scale open parallel corpus with document context extracted from ParaCrawl in five language pairs, along with code to compile document-level datasets for any language pair supported by ParaCrawl. We train context-aware models on these datasets and find improvements in terms of overall translation quality and targeted document-level phenomena. We also analyse how much long-range information is useful to model some of these discourse phenomena and find models are able to utilise context from several preceding sentences."
 }
 ```