Improve dataset card with paper link, task category, and citation

This PR improves the dataset card by:

- Adding a link to the associated paper: [Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data](https://arxiv.org/abs/2506.00469)
- Specifying the `text-generation` task category in the metadata.
- Correcting and updating the citation to reflect the paper.

README.md

@@ -1,5 +1,11 @@
 ---
 license: odc-by
+task_categories:
+  - text-generation
+tags:
+  - multilingual
+  - translation
+  - low-resource
 ---
 
 # MaLA Corpus: Massive Language Adaptation Corpus
@@ -8,7 +14,7 @@ This is a cleaned version with some necessary data cleaning.
 
 ## Dataset Summary
 
-The **MaLA Corpus** (Massive Language Adaptation) is a comprehensive, multilingual dataset designed to support the continual pre-training of large language models. It covers **939 languages** and consists of over **74 billion tokens**, making it one of the largest datasets of its kind. With a focus on improving the representation of low-resource languages, the MaLA Corpus is a critical resource for advancing multilingual models, particularly those aimed at serving underrepresented languages.
+The **MaLA Corpus** (Massive Language Adaptation) is a comprehensive, multilingual dataset designed to support the continual pre-training of large language models. It covers **939 languages** and consists of over **74 billion tokens**, making it one of the largest datasets of its kind. With a focus on improving the representation of low-resource languages, the MaLA Corpus is a critical resource for advancing multilingual models, particularly those aimed at serving underrepresented languages. This dataset supports the work presented in [Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data](https://arxiv.org/abs/2506.00469).
 
 ---
 
@@ -57,12 +63,12 @@ We will comply with legitimate requests by removing the affected sources from th
 ## Citation
 
 ```
-@article{ji2024emma500enhancingmassivelymultilingual,
-      title={{EMMA}-500: Enhancing Massively Multilingual Adaptation of Large Language Models}, 
-      author={Shaoxiong Ji and Zihao Li and Indraneil Paul and Jaakko Paavola and Peiqin Lin and Pinzhen Chen and Dayyán O'Brien and Hengyu Luo and Hinrich Schütze and Jörg Tiedemann and Barry Haddow},
-      year={2024},
-      journal={arXiv preprint 2409.17892},
-      url={https://arxiv.org/abs/2409.17892}, 
+@article{ji2025massivelymultilingualadaptation,
+      title={Massively Multilingual Adaptation of Large Language Models Using Bilingual Translation Data}, 
+      author={Shaoxiong Ji, Zihao Li, Jaakko Paavola, Indraneil Paul, Hengyu Luo, and Jörg Tiedemann},
+      year={2025},
+      journal={arXiv preprint arXiv:2506.00469},
+      url={https://arxiv.org/abs/2506.00469}, 
 }
 ```
 
@@ -72,3 +78,5 @@ We will comply with legitimate requests by removing the affected sources from th
 We extend our thanks to the language communities and contributors who helped source, clean, and validate the diverse data used in the MaLA Corpus. Their efforts are invaluable in supporting linguistic diversity in AI research.
 
 This work is done by researchers at [Helsinki-NLP](https://huggingface.co/Helsinki-NLP) in collaboration with partners from TU Darmstadt, the University of Edinburgh, and LMU Munich. It is funded by [HPLT](https://hplt-project.org) and [UTTER](https://he-utter.eu).
+
+[Github](https://github.com/mala-lm/emma-500) | [Project Page](https://mala-lm.github.io/emma-500-gen2)