Update README.md

README.md

@@ -5,7 +5,7 @@ In machine translation, historical models were incapable of handling longer cont
 Now, despite the emergence of long-sequence methods, we remain within a sentence-level paradigm and without data to adequately approach context-aware machine translation. 
 Most large-scale datasets have been processed through a pipeline that discards document-level metadata. 
 
-[ParaDocs](https://arxiv.org/abs/2406.03869) is a publicly available dataset that produces parallel annotations for the document-level metadata of three large publicly available corpora (ParaCrawl, Europal, and News Commentary) in many languages.
+[ParaDocs](https://aclanthology.org/2024.findings-acl.589/) is a publicly available dataset that produces parallel annotations for the document-level metadata of three large publicly available corpora (ParaCrawl, Europal, and News Commentary) in many languages.
 Using this data and the following scripts, you can download parallel document contexts for the purpose of training context-aware machine translation systems.
 
 If you have questions about this data or use of the following scripts, please do not hesitate to contact the maintainer at rewicks@jhu.edu.
@@ -36,7 +36,7 @@ The filtering commandline arguments are explained in more detial in Section 3.2.
 
 ## The Paper
 
-If you use this dataset in your research. Please cite our [paper](https://arxiv.org/abs/2406.03869).
+If you use this dataset in your research. Please cite our [paper](https://aclanthology.org/2024.findings-acl.589/).
 
 
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