edited the citations

README.md

@@ -14,11 +14,26 @@ The dataset is released under the terms of [ODC-BY](https://opendatacommons.org/
 
 
 ### Citation Information
-
-Schwenk et al, CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web. ACL https://aclanthology.org/2021.acl-long.507/
-Hefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv https://arxiv.org/abs/2205.12654, 2022.<br>
-NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv https://arxiv.org/abs/2207.04672, 2022.
-
+```
+@inproceedings{ranathunga-etal-2024-quality,
+    title = "Quality Does Matter: A Detailed Look at the Quality and Utility of Web-Mined Parallel Corpora",
+    author = "Ranathunga, Surangika  and
+      De Silva, Nisansa  and
+      Menan, Velayuthan  and
+      Fernando, Aloka  and
+      Rathnayake, Charitha",
+    editor = "Graham, Yvette  and
+      Purver, Matthew",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = mar,
+    year = "2024",
+    address = "St. Julian{'}s, Malta",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.eacl-long.52",
+    pages = "860--880",
+    abstract = "We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are significant quality differences between different portions of web-mined corpora and that the quality varies across languages and datasets. We also show that, for some web-mined datasets, Neural Machine Translation (NMT) models trained with their highest-ranked 25k portion can be on par with human-curated datasets.",
+}
+```
 ### Contributions
 
 We thank the NLLB Meta AI team for open sourcing the meta data and instructions on how to use it with special thanks to  Bapi Akula, Pierre Andrews, Onur Çelebi, Sergey Edunov, Kenneth Heafield, Philipp Koehn, Alex Mourachko, Safiyyah Saleem, Holger Schwenk, and Guillaume Wenzek. We also thank the AllenNLP team at AI2 for hosting and releasing this data, including Akshita Bhagia (for engineering efforts to host the data, and create the huggingface dataset), and Jesse Dodge (for organizing the connection).