Update ChinaOpen.py

ChinaOpen.py

@@ -8,30 +8,46 @@ from datasets.tasks import QuestionAnsweringExtractive
 logger = datasets.logging.get_logger(__name__)
 
 _CITATION = """\
-@article{arXiv:2305.05880,
-       author = {Chen, Aozhu and Wang, Ziyuan and Dong, Chengbo and Tian, Kaibin and Zhao, Ruixiang and Liang, Xun and Kang, Zhanhui and Li, Xirong},
-        title = {ChinaOpen: A Dataset for Open-World Multimodal Learning},
-      journal = {arXiv e-prints},
-         year = 2023,
-          eid = {arXiv:2305.05880},
-        pages = {arXiv:2305.05880},
-archivePrefix = {arXiv},
-       eprint = {2305.05880},
+@inproceedings{10.1145/3581783.3612156,
+    author = {Chen, Aozhu and Wang, Ziyuan and Dong, Chengbo and Tian, Kaibin and Zhao, Ruixiang and Liang, Xun and Kang, Zhanhui and Li, Xirong},
+    title = {ChinaOpen: A Dataset for Open-World Multimodal Learning},
+    year = {2023},
+    isbn = {9798400701085},
+    publisher = {Association for Computing Machinery},
+    address = {New York, NY, USA},
+    url = {https://doi.org/10.1145/3581783.3612156},
+    doi = {10.1145/3581783.3612156},
+    booktitle = {Proceedings of the 31st ACM International Conference on Multimedia},
+    pages = {6432–6440},
+    numpages = {9},
+    keywords = {chinese video dataset, multi-task evaluation, multimodal learning},
+    location = {Ottawa ON, Canada},
+    series = {MM '23}
 }
 """
 
+
+
 _DESCRIPTION = """\
-Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
-dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
-articles, where the answer to every question is a segment of text, or span, \
-from the corresponding reading passage, or the question might be unanswerable.
+ChinaOpen is a dataset sourced from Bilibili, a popular Chinese video-sharing website, \
+for open-world multimodal learning. While the state-of-the-art multimodal learning \
+networks have shown impressive performance in automated video annotation and \
+cross-modal video retrieval, their training and evaluation are primarily conducted on \
+YouTube videos with English text. Their effectiveness on Chinese data remains to be \
+verified. For a multi-faceted evaluation, we build ChinaOpen-1k, a manually labeled \
+test set of 1k videos. Each test video is accompanied with a manually checked user \
+title and a manually written caption. Besides, each video is manually tagged to describe \
+objects / actions / scenes shown in the visual content. The original user tags are also \
+manually checked. Moreover, with all the Chinese text translated into English, \
+ChinaOpen-1k is also suited for evaluating models trained on English data. \   
+
 """
 
 
 
 #_URL = "./ChinaOpen-1k.zip"
 _URLS = {
-    "train": "https://huggingface.co/datasets/AIMClab/ChinaOpen/tree/main/data"
+    "train": "https://huggingface.co/datasets/AIMClab/ChinaOpen/resolve/main/ChinaOpen-1k.zip"
 }