Update README.md

README.md

@@ -21,13 +21,59 @@ dataset_info:
     dtype: string
   splits:
   - name: test
-    num_bytes: 256512910.0
+    num_bytes: 256512910
     num_examples: 1121
   download_size: 235090892
-  dataset_size: 256512910.0
+  dataset_size: 256512910
 configs:
 - config_name: default
   data_files:
   - split: test
     path: data/test-*
+license: apache-2.0
+language:
+- ko
+- en
+tags:
+- speech
+- recognition
+- code-switching
 ---
+
+# HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition
+> [Gio Paik](https://sites.google.com/view/giopaik)\*, [Yongbeom Kim](https://bayle0627.github.io/), [Soungmin Lee](https://minovermax.github.io/), [Sangmin Ahn](https://www.linkedin.com/in/sangmin-ahn-0656ab1b1/)†, and [Chanwoo Kim](https://www.linkedin.com/in/chanwkim)†, *Under Review*    
+> \* Corresponding Author, † Equal Contribution
+
+[**✨ Code**](https://github.com/ThetaOne-AI/HiKE) | [**🤗 Dataset**](https://huggingface.co/datasets/thetaone-ai/HiKE) | [**📖 Paper**](https://arxiv.org/abs/2509.24613)
+
+## Introduction
+HiKE is the first Korean-English Code-Switching (CS) Automatic Speech Recognition (ASR) benchmark composed of high-quality, natural CS data across various topics. We use **Mixed Error Rate (MER)** and **Point of Interest Error Rate (PIER)** [1] to precisely evaluate the models' CS ASR capability.
+
+Experimental results show that all multilingual ASR models exhibit significantly higher error rates on code-switching data, and that their CS-ASR capabilities can be improved through fine-tuning.
+
+For further details, please refer to [our paper]().
+
+[1] Ugan et al., [“PIER: A Novel Metric for Evaluating What Matters in Code-Switching”](https://arxiv.org/abs/2501.09512), ICASSP 2025 
+
+### Hierarchical CS-Level Labels
+To provide more fine-grained comparison of model performance on different forms of code-switching, we labeled each utterance according to the following levels:
+
+- Word-level CS: Code-switching that occurs at the word level, typically as the substitution of a single noun or adjective.
+- Phrase-level CS: Occurs when a multi-word phrase within a sentence appears in another language.
+- Sentence-level CS: The alternation between languages on a sentence-by-sentence basis.
+
+### Loanword Labels
+Loanwords are words adopted from a foreign language and adapted to the phonology and orthography of the new language. For example, the Korean loanword **'버스' [bəs]** and the English word **'bus' [bʌs]** are pronounced almost identically and can be used interchangeably in a CS context. To avoid this problem, we meticulously labeled all loanwords contained in our dataset.
+
+## Citation
+```
+@misc{paik2025hike,
+      title={{HiKE}: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition}, 
+      author={Gio Paik and Yongbeom Kim and Soungmin Lee and Sangmin Ahn and Chanwoo Kim},
+      year={2025},
+      eprint={2509.24613},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2509.24613}, 
+}
+```