Update README.md

README.md

@@ -26,4 +26,27 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-sa-4.0
+language:
+- ko
+tags:
+- safety
 ---
+
+reference: [https://github.com/jason9693/APEACH](https://github.com/jason9693/APEACH)
+```
+@inproceedings{yang-etal-2022-apeach,
+    title = "{APEACH}: Attacking Pejorative Expressions with Analysis on Crowd-Generated Hate Speech Evaluation Datasets",
+    author = "Yang, Kichang  and
+      Jang, Wonjun  and
+      Cho, Won Ik",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, United Arab Emirates",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2022.findings-emnlp.525",
+    pages = "7076--7086",
+    abstract = "In hate speech detection, developing training and evaluation datasets across various domains is the critical issue. Whereas, major approaches crawl social media texts and hire crowd-workers to annotate the data. Following this convention often restricts the scope of pejorative expressions to a single domain lacking generalization. Sometimes domain overlap between training corpus and evaluation set overestimate the prediction performance when pretraining language models on low-data language. To alleviate these problems in Korean, we propose APEACH that asks unspecified users to generate hate speech examples followed by minimal post-labeling. We find that APEACH can collect useful datasets that are less sensitive to the lexical overlaps between the pretraining corpus and the evaluation set, thereby properly measuring the model performance.",
+}
+```