@inproceedings{wong-etal-2024-humanistic,
    title = "Humanistic Buddhism Corpus: A Challenging Domain-Specific Dataset of {E}nglish Translations for Classical and {M}odern {C}hinese",
    author = "Wong, Youheng W.  and
      Parde, Natalie  and
      Koyuncu, Erdem",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.737",
    pages = "8406--8417",
    abstract = "We introduce the Humanistic Buddhism Corpus (HBC), a dataset containing over 80,000 Chinese-English parallel phrases extracted and translated from publications in the domain of Buddhism. HBC is one of the largest free domain-specific datasets that is publicly available for research, containing text from both classical and modern Chinese. Moreover, since HBC originates from religious texts, many phrases in the dataset contain metaphors and symbolism, and are subject to multiple interpretations. Compared to existing machine translation datasets, HBC presents difficult unique challenges. In this paper, we describe HBC in detail. We evaluate HBC within a machine translation setting, validating its use by establishing performance benchmarks using a Transformer model with different transfer learning setups.",
}

We introduce the Humanistic Buddhism Corpus (HBC), a dataset containing over 80,000 Chinese-English parallel phrases extracted and translated from publications in the domain of Buddhism. HBC is one of the largest free domain-specific datasets that is publicly available for research, containing text from both classical and modern Chinese. Moreover, since HBC originates from religious texts, many phrases in the dataset contain metaphors and symbolism, and are subject to multiple interpretations. Compared to existing machine translation datasets, HBC presents difficult unique challenges. In this paper, we describe HBC in detail. We evaluate HBC within a machine translation setting, validating its use by establishing performance benchmarks using a Transformer model with different transfer learning setups.
Wong, Youheng W. Parde, Natalie Koyuncu, Erdem
Humanistic Buddhism Corpus: A Challenging Domain-Specific Dataset of English Translations for Classical and Modern Chinese