raptorkwok/ccpc-dataset-v2.1

CCPC Dataset version 2.1 - Cantonese-Written Chinese Parallel Corpus

Data Size

Total Data Size: 848,206

Dataset Split	Sentence Count	Columns	Description
Train	776,790	yue, zh, ref (empty)	Train Set
Validation	64,224	yue, zh, ref (empty)	Validation Set
Test	7,192	yue, zh (empty), ref	Test Set, "ref" column contains ground truth

• "yue" column: Cantonese sentences
• "zh" column: Written Chinese sentence
• "ref" column: Ground truth in Written Chinese

Citation

@inproceedings{ccpc-dataset,
    author = {Kwok, Raptor Yick-Kan and Au Yeung, Siu-Kei and Li, Zongxi and Hung, Kevin},
    title = {CCPC: A Large-Scale Cantonese-Chinese Parallel Corpus for Cantonese Translation},
    year = {2026},
    publisher = {IEEE},
    keywords = {Cantonese, Written Chinese, neural networks, translation},
    location = {Kota Kinabalu, Malaysia}
}
@inproceedings{10.1145/3639233.3639332,
    author = {Kwok, Raptor Yick-Kan and Au Yeung, Siu-Kei and Li, Zongxi and Hung, Kevin},
    title = {Cantonese to Written Chinese Translation via HuggingFace Translation Pipeline},
    year = {2024},
    isbn = {9798400709227},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3639233.3639332},
    doi = {10.1145/3639233.3639332},
    abstract = {Cantonese, a low-resource language [5] that has been used in Southeastern China for hundreds of years, with over 85 million native speakers worldwide, is poorly supported in the mainstream language model for existing translation platforms such as Baidu, Google and Bing. This paper presents a large parallel corpus of 130 thousand Cantonese and Written Chinese pairs. The data are used to train a translation model using the translation pipeline of the Hugging Face Transformers architecture, a dominant architecture for natural language processing nowadays [18]. The BLEU score and manual assessment evaluate the performance. The translation results achieve a BLEU score of 41.35and chrF++ score of 44.88on the entire validation set. The model also works reasonably well with long sentences of over 20 Chinese characters. It achieves a BLEU score of 48.61and chrF++ score of 39.87on long sentences. Those results are comparable with the existing Baidu Fanyi and Bing Translate. We also establish a Cantonese sentence evaluation metric to classify the quality of the source Cantonese sentence by professional translators. We then compare the BLEU and chrF++ scores with the corresponding evaluation score and found that the better the quality of the source sentence, the higher the BLEU and chrF++ scores. Last, we proved that our corpus enabled the Cantonese translation capability of the Chinese BART pre-trained model.},
    booktitle = {Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval},
    pages = {77–84},
    numpages = {8},
    keywords = {Cantonese, Written Chinese, neural networks, translation},
    location = {Seoul, Republic of Korea},
    series = {NLPIR '23}
}
@misc {cantoneseWrittenChineseTranslationsGen3,
    author = {Kwok, R.},
    title = {Cantonese-Written Chinese Parallel Corpus (CCPC)},
    year = {2024},
    url = {https://huggingface.co/datasets/raptorkwok/cantonese-chinese-parallel-corpus},
}