Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- en
+# Add other specific ISO language codes your dataset supports, e.g., fr, de, zh
+multilinguality:
+- multilingual
+task_categories:
+- automatic-speech-recognition
+- translation
+- text-generation
+tags:
+- speech-to-text
+- generative-error-correction
+- n-best-list
+- covost2
+- common-voice
+---
+
+# CoVoGER: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models
+
+## Dataset Description
+
+Large language models (LLMs) can rewrite the N-best hypotheses from a speech-to-text model, often fixing recognition or translation errors that traditional rescoring cannot. Yet research on generative error correction (GER) has been focusing on monolingual automatic speech recognition (ASR), leaving its multilingual and multitask potential underexplored. 
+
+We introduce **CoVoGER**, a benchmark for GER that covers both ASR and speech-to-text translation (ST) across 15 languages and 28 language pairs. CoVoGER is constructed by decoding Common Voice 20.0 and CoVoST-2 with Whisper of three model sizes and SeamlessM4T of two model sizes, providing 5-best lists obtained via a mixture of beam search and temperature sampling.
+
+- **Paper:** [CoVoGER: A Multilingual Multitask Benchmark...](https://aclanthology.org/2025.emnlp-main.320/)
+- **Repository:** [GitHub - N-Orien/CoVoGER](https://github.com/N-Orien/CoVoGER)
+- **Conference:** EMNLP 2025
+
+## Usage and Data Commands
+
+You can easily download and load the dataset using the Hugging Face `datasets` library in Python.
+
+### Terminal/CLI Commands
+If you want to download the repository locally via the Hugging Face CLI, run:
+```bash
+# Ensure you have git-lfs installed
+git lfs install
+git clone https://huggingface.co/datasets/PeacefulData/CoVoGER
+```
+
+
+```bib
+@inproceedings{yang-etal-2025-covoger,
+    title = "{C}o{V}o{GER}: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models",
+    author = "Yang, Zhengdong  and
+      Wan, Zhen  and
+      Li, Sheng  and
+      Yang, Chao-Han Huck  and
+      Chu, Chenhui",
+    editor = "Christodoulopoulos, Christos  and
+      Chakraborty, Tanmoy  and
+      Rose, Carolyn  and
+      Peng, Violet",
+    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "[https://aclanthology.org/2025.emnlp-main.320/](https://aclanthology.org/2025.emnlp-main.320/)",
+    pages = "6313--6325",
+    isbn = "979-8-89176-332-6"
+}