Update README.md

README.md

@@ -55,12 +55,26 @@ print(dataset[0])
 # Citation
 If you use this corpus, please cite:
 ```text
-  @inproceedings{
-    knafou2025transbert,
-    title={Trans{BERT}: A Framework for Synthetic Translation in Domain-Specific Language Modeling},
-    author={Julien Knafou and Luc Mottin and Ana{\"\i}s Mottaz and Alexandre Flament and Patrick Ruch},
-    booktitle={The 2025 Conference on Empirical Methods in Natural Language Processing},
-    year={2025},
-    url={https://transbert.s3.text-analytics.ch/TransBERT.pdf}
+  @inproceedings{knafou-etal-2025-transbert,
+      title = "{T}rans{BERT}: A Framework for Synthetic Translation in Domain-Specific Language Modeling",
+      author = {Knafou, Julien  and
+        Mottin, Luc  and
+        Mottaz, Ana{\"i}s  and
+        Flament, Alexandre  and
+        Ruch, Patrick},
+      editor = "Christodoulopoulos, Christos  and
+        Chakraborty, Tanmoy  and
+        Rose, Carolyn  and
+        Peng, Violet",
+      booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
+      month = nov,
+      year = "2025",
+      address = "Suzhou, China",
+      publisher = "Association for Computational Linguistics",
+      url = "https://aclanthology.org/2025.findings-emnlp.1053/",
+      doi = "10.18653/v1/2025.findings-emnlp.1053",
+      pages = "19338--19354",
+      ISBN = "979-8-89176-335-7",
+      abstract = "The scarcity of non-English language data in specialized domains significantly limits the development of effective Natural Language Processing (NLP) tools. We present TransBERT, a novel framework for pre-training language models using exclusively synthetically translated text, and introduce TransCorpus, a scalable translation toolkit. Focusing on the life sciences domain in French, our approach demonstrates that state-of-the-art performance on various downstream tasks can be achieved solely by leveraging synthetically translated data. We release the TransCorpus toolkit, the TransCorpus-bio-fr corpus (36.4GB of French life sciences text), TransBERT-bio-fr, its associated pre-trained language model and reproducible code for both pre-training and fine-tuning. Our results highlight the viability of synthetic translation in a high-resource translation direction for building high-quality NLP resources in low-resource language/domain pairs."
   }
 ```