Add link to paper

This PR ensures the dataset is discoverable through https://huggingface.co/papers/2409.14074

README.md

@@ -1,4 +1,13 @@
 ---
+language:
+- vi
+- en
+- de
+- fr
+- zh
+license: mit
+task_categories:
+- automatic-speech-recognition
 viewer: true
 dataset_info:
 - config_name: Chinese
@@ -148,15 +157,6 @@ configs:
     path: Vietnamese/test-*
   - split: dev
     path: Vietnamese/dev-*
-language:
-- vi
-- en
-- de
-- fr
-- zh
-license: mit
-task_categories:
-- automatic-speech-recognition
 tags:
 - medical
 ---
@@ -169,8 +169,9 @@ This technology enhances patient care by enabling efficient communication across
 In this work, we introduce *MultiMed*, a collection of small-to-large end-to-end ASR models for the medical domain, spanning five languages: Vietnamese, English, German, French, and Mandarin Chinese, together with the corresponding real-world ASR dataset. 
 To our best knowledge, *MultiMed* stands as **the largest and the first multilingual medical ASR dataset**, in terms of total duration, number of speakers, diversity of diseases, recording conditions, speaker roles, unique medical terms, accents, and ICD-10 codes. 
 
+[Paper](https://huggingface.co/papers/2409.14074)
 
-Please cite this paper: [https://arxiv.org/abs/2409.14074](https://arxiv.org/abs/2409.14074)
+Please cite this paper: 
 
     @inproceedings{le2024multimed,
       title={MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder},